有机小分子吸收能的精确计算：神经网络与支持向量机方法
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摘要
分子吸收能可以表征分子的内在结构信息和电子性质,是分子的一个重要物理属性。因此,精确地预测吸收能是计算化学领域的一个重要问题。量子化学是研究分子微观结构、性质和分子间相互作用的基础学科,在近20年中,量子化学的基础理论和计算方法取得了显著进展。量子化学计算的一大优势在于它可以先于实验来预测物质的性质或实验上至今无法测得的一些物理量及无法观测到的反应过程。量子化学计算在解释和预测中小分子的实验结果上显示出巨大优势,但由于计算方法固有的近似使得误差不可避免,尤其是对于结构不规整的复杂大分子,计算误差更大。在近10年里,许多统计校正方法被用于提高量子化学的计算精度。其步骤是先用量子化学方法计算分子的相关物理参数,然后用统计方法来确定理论计算和实验值的数量关系,这些方法主要包括:多元线性回归和人工神经网络等。
本论文针对160个有机小分子体系,用人工神经网络和支持向量机等方法来校正量子化学方法计算的结果,提高了量子化学计算吸收能的精度。在简单的物理参数下,组合型计算方法能够减小理论计算因忽略电子相关效应和使用小基组所带来的系统误差,为准确、快捷地预测分子性质提供了一种新的研究手段。
研究工作主要包括如下几个部分:
1、量子化学方法计算有机小分子的紫外可见吸收光谱,用遗传算法优化BP神经网络(GABP)来提高有机小分子吸收能的计算精度。GABP1分别校正了三种量子化学计算方法:B3LYP/6-31G(d),B3LYP/STO-3G和ZINDO,校正前三种方法得到的均方根误差分别是0.32,0.95和0.46 eV,校正后均方根误差降低到了0.14,0.19和0.18 eV。其中,B3LYP/6-31G(d)-GABP1计算结果误差较小,基本与实验值吻合。
2、GABP2用于确定B3LYP/6-31G(d)-GABP1计算值和低水平理论计算方法之间的数量关系,例如B3LYP/STO-3G和ZINDO。GABP2校正B3LYP/STO-3G和ZINDO后误差的均方根降低到了0.20和0.19 eV。比较两次校正结果,我们可以验证GABP方法的有效性和可行性。显然,B3LYP/6-31G(d)-GABP1可以有效的提高有机小分子吸收能的精度,并且在实验结果不可获得或者不可得到的情况下,B3LYP/6-31G(d)-GABP1的校正值可以看做是实验的近似值。
3、B3LYP/6-31G(d)-GABP方法可以分析较大分子的吸收能,当大分子缺少实验值或者无法获得较高精度理论值的时候,它可能成为一个预测实验值的有效的理论计算工具。
4、根据所选数据集来增加物理参数,然后用多元线性回归来筛选物理参数。
5、引入最小二乘支持向量机来提高密度泛函理论计算值。最小二乘支持向量机校正后的误差由0.32 eV降低到了0.11 eV。相比多元线性回归来说,最小二乘支持向量机有更好的适应能力和校正效果。在无法获得实验值或无法得到精度较高的量子化学计算结果的时候,最小二乘支持向量机校正B3LYP/6-31G(d)的方法可以有效的预测分子的吸收能。最小二乘支持向量机扩展了B3LYP/6-31G(d)的真实性和实用性。
Absorption energy is a significant physical property for a molecule, which implies inherent structure information and electronic properties. The accurate prediction the absorption energy is one of the important topics in computational chemistry. Quantum chemistry is a fundamental subject studying properties and interactions of molecules. In the past decades, it has been developed remarkably on its primary theories and methods. One of the Holy Grails of quantum mechanical calculation is to predict properties of matter prior to experiments, to examine the physical properties or processes that are inaccessible by experiments. Despite their success, the results of quantum mechanical calculation contain inherent numerical errors caused by various intrinsic approximations, in particular for complex systems. During the last 10 years, many statistical correction approaches were employed to improve the results of quantum chemical method. After the theoretical calculation of the properties of molecules, statistical correction approaches can be used to determine the quantitative relationship between the calculated and experimental results. These statistical correction approaches mainly include multiple linear regression (MLR) or nonlinear methods et al.
In the present work, neural network and least squares support vector machine have been applied to improve the calculation accuracy of quantum chemical methods for absorption energies of 160 small organic molecules. With general descriptors, these combined methods can greatly eliminate the systemic errors of theoretical calculation due to ignoring the electron correlation and using small basis set, and will be a novel tool for predicting the properties of the molecules.
Our work has been focus on following aspects:
1. The combination of genetic algorithm and back propagation neural network correction approach (GABP) has successfully improved the calculation accuracy of the absorption energies after quantum chemical methods calculated UV-visible absorption spectra of 160 small organic molecules. Firstly, the GABP1 is introduced to determine the quantitative relationship between the experimental results and calculations obtained by using quantum chemical methods. After GABP1 correction, the root-mean-square (RMS) deviations of the calculated absorption energies reduce from 0.32, 0.95 and 0.46 eV to 0.14, 0.19 and 0.18 eV for B3LYP/6-31G(d), B3LYP/STO-3G and ZINDO methods, respectively. The corrected results of B3LYP/6-31G(d)-GABP1 are in good agreement with experimental results.
2. The GABP2 is introduced to determine the quantitative relationship between the results of B3LYP/6-31G(d)-GABP1 method and calculations of the low accuracy methods (B3LYP/STO-3G and ZINDO). After GABP2 correction, the RMS deviations of the calculated absorption energies reduce to 0.20 and 0.19 eV for B3LYP/STO-3G and ZINDO methods, respectively. The results show that the RMS deviations after GABP1 and GABP2 correction are similar for B3LYP/STO-3G and ZINDO methods. Thus, the B3LYP/6-31G(d)-GABP1 is a better method to predict absorption energies and can be used as the approximation of experimental results where the experimental results are unknown or uncertain by experimental methods.
3. This GABP may be used for predicting absorption energies of larger organic molecules that are unavailable by experimental methods and by high accuracy theoretical methods with lager basis sets. Thus, this method is a realiable tool to predict absorption energies.
4. We enlarge the descriptors according to the data set. After multiple linear regression, 8 descriptors are selected as the proper physical descriptors.
5. In this paper we introduce least squares support vector machine (LS-SVM) to improve the calculation accuracy of density functional theory. Upon the LS-SVM approach, the RMS deviations of the B3LYP/6-31G(d) calculated absorption energies of 160 organic molecules are reduced from 0.32 eV to 0.11 eV. Comparison of the MLR and LS-SVM values demonstrates the feasibility and effectiveness of the LS-SVM approach. And, the LS-SVM correction on top of the B3LYP/6-31G(d) results is a better method to predict absorption energies and can be used as the approximation of experimental results when the experimental results are limited to measurement with very high accuracy. LS-SVM greatly extends the reliability and applicability of the B3LYP/6-31G(d) method.
本论文针对160个有机小分子体系,用人工神经网络和支持向量机等方法来校正量子化学方法计算的结果,提高了量子化学计算吸收能的精度。在简单的物理参数下,组合型计算方法能够减小理论计算因忽略电子相关效应和使用小基组所带来的系统误差,为准确、快捷地预测分子性质提供了一种新的研究手段。
研究工作主要包括如下几个部分:
1、量子化学方法计算有机小分子的紫外可见吸收光谱,用遗传算法优化BP神经网络(GABP)来提高有机小分子吸收能的计算精度。GABP1分别校正了三种量子化学计算方法:B3LYP/6-31G(d),B3LYP/STO-3G和ZINDO,校正前三种方法得到的均方根误差分别是0.32,0.95和0.46 eV,校正后均方根误差降低到了0.14,0.19和0.18 eV。其中,B3LYP/6-31G(d)-GABP1计算结果误差较小,基本与实验值吻合。
2、GABP2用于确定B3LYP/6-31G(d)-GABP1计算值和低水平理论计算方法之间的数量关系,例如B3LYP/STO-3G和ZINDO。GABP2校正B3LYP/STO-3G和ZINDO后误差的均方根降低到了0.20和0.19 eV。比较两次校正结果,我们可以验证GABP方法的有效性和可行性。显然,B3LYP/6-31G(d)-GABP1可以有效的提高有机小分子吸收能的精度,并且在实验结果不可获得或者不可得到的情况下,B3LYP/6-31G(d)-GABP1的校正值可以看做是实验的近似值。
3、B3LYP/6-31G(d)-GABP方法可以分析较大分子的吸收能,当大分子缺少实验值或者无法获得较高精度理论值的时候,它可能成为一个预测实验值的有效的理论计算工具。
4、根据所选数据集来增加物理参数,然后用多元线性回归来筛选物理参数。
5、引入最小二乘支持向量机来提高密度泛函理论计算值。最小二乘支持向量机校正后的误差由0.32 eV降低到了0.11 eV。相比多元线性回归来说,最小二乘支持向量机有更好的适应能力和校正效果。在无法获得实验值或无法得到精度较高的量子化学计算结果的时候,最小二乘支持向量机校正B3LYP/6-31G(d)的方法可以有效的预测分子的吸收能。最小二乘支持向量机扩展了B3LYP/6-31G(d)的真实性和实用性。
Absorption energy is a significant physical property for a molecule, which implies inherent structure information and electronic properties. The accurate prediction the absorption energy is one of the important topics in computational chemistry. Quantum chemistry is a fundamental subject studying properties and interactions of molecules. In the past decades, it has been developed remarkably on its primary theories and methods. One of the Holy Grails of quantum mechanical calculation is to predict properties of matter prior to experiments, to examine the physical properties or processes that are inaccessible by experiments. Despite their success, the results of quantum mechanical calculation contain inherent numerical errors caused by various intrinsic approximations, in particular for complex systems. During the last 10 years, many statistical correction approaches were employed to improve the results of quantum chemical method. After the theoretical calculation of the properties of molecules, statistical correction approaches can be used to determine the quantitative relationship between the calculated and experimental results. These statistical correction approaches mainly include multiple linear regression (MLR) or nonlinear methods et al.
In the present work, neural network and least squares support vector machine have been applied to improve the calculation accuracy of quantum chemical methods for absorption energies of 160 small organic molecules. With general descriptors, these combined methods can greatly eliminate the systemic errors of theoretical calculation due to ignoring the electron correlation and using small basis set, and will be a novel tool for predicting the properties of the molecules.
Our work has been focus on following aspects:
1. The combination of genetic algorithm and back propagation neural network correction approach (GABP) has successfully improved the calculation accuracy of the absorption energies after quantum chemical methods calculated UV-visible absorption spectra of 160 small organic molecules. Firstly, the GABP1 is introduced to determine the quantitative relationship between the experimental results and calculations obtained by using quantum chemical methods. After GABP1 correction, the root-mean-square (RMS) deviations of the calculated absorption energies reduce from 0.32, 0.95 and 0.46 eV to 0.14, 0.19 and 0.18 eV for B3LYP/6-31G(d), B3LYP/STO-3G and ZINDO methods, respectively. The corrected results of B3LYP/6-31G(d)-GABP1 are in good agreement with experimental results.
2. The GABP2 is introduced to determine the quantitative relationship between the results of B3LYP/6-31G(d)-GABP1 method and calculations of the low accuracy methods (B3LYP/STO-3G and ZINDO). After GABP2 correction, the RMS deviations of the calculated absorption energies reduce to 0.20 and 0.19 eV for B3LYP/STO-3G and ZINDO methods, respectively. The results show that the RMS deviations after GABP1 and GABP2 correction are similar for B3LYP/STO-3G and ZINDO methods. Thus, the B3LYP/6-31G(d)-GABP1 is a better method to predict absorption energies and can be used as the approximation of experimental results where the experimental results are unknown or uncertain by experimental methods.
3. This GABP may be used for predicting absorption energies of larger organic molecules that are unavailable by experimental methods and by high accuracy theoretical methods with lager basis sets. Thus, this method is a realiable tool to predict absorption energies.
4. We enlarge the descriptors according to the data set. After multiple linear regression, 8 descriptors are selected as the proper physical descriptors.
5. In this paper we introduce least squares support vector machine (LS-SVM) to improve the calculation accuracy of density functional theory. Upon the LS-SVM approach, the RMS deviations of the B3LYP/6-31G(d) calculated absorption energies of 160 organic molecules are reduced from 0.32 eV to 0.11 eV. Comparison of the MLR and LS-SVM values demonstrates the feasibility and effectiveness of the LS-SVM approach. And, the LS-SVM correction on top of the B3LYP/6-31G(d) results is a better method to predict absorption energies and can be used as the approximation of experimental results when the experimental results are limited to measurement with very high accuracy. LS-SVM greatly extends the reliability and applicability of the B3LYP/6-31G(d) method.
引文
[1]林梦海.量子化学计算方法与应用[M].北京:科学出版社,2004.
[2]Wang X J,Wong L H,Hu L H,et al.Improving the accuracy of density-functional theory calculation: The statistical correction approach[J].J Phys Chem A,2004.108(40):8514-8525.
[3]段雪梅.分子能量和热力学函数的精确计算——统计的校正方法[D]:[博士学位论文].上海:复旦大学,(2005).
[4]Foresman J B,Frisch E.Exploring Chemistry with Electronic Structure Methos[M]:Gaussian Inc,1996.
[5]Ghouri M M,Singh S,Ramachandran B.Scaled density functional theory correlation functionals[J].Journal of Physical Chemistry A,2007.111(41):10390-10399.
[6]Jacquemin D,Wathelet V,Perpete E A.Ab initio investigation of the n ->pi* transitions in thiocarbonyl dyes[J].Journal of Physical Chemistry A,2006.110(29):9145-9152.
[7]Jacquemin D,Perpete E A,Vydrov O A,et al.Assessment of long-range corrected functionals performance for n ->pi(*) transitions in organic dyes[J].Journal of Chemical Physics,2007.127(9):6.
[8]Duan X M,Song G L,Li Z H,et al.Accurate prediction of heat of formation by combining Hartree-Fock/density functional theory calculation with linear regression correction approach[J].J. Chem. Phys.,2004.121(15):7086-7095.
[9]Duan X M,Li Z H,Hu H R,et al.Linear regression correction to first principle theoretical calculations - Improved descriptors and enlarged training set[J].Chem. Phys. Lett.,2005.409(4-6):315-321.
[10]Liu G Z,Mi Z T.Prediction of pulsation frequency of pulsing flow in trickle-bed reactors using artificial neural network[J].Chemical Engineering Science,2004.59(24):5787-5794.
[11]Qu N,Wang L H,Zhu M C,et al.Radial basis function networks combined with genetic algorithm applied to nondestructive determination of compound erythromycin ethylsuccinate powder[J].Chemometrics and Intelligent Laboratory Systems,2008.90(2):145-152.
[12]Xu W W,Liu X G,Luan S X,et al.An ab initio potential energy surface of the He+H-2(+) -> HeH++H reaction[J].Chem Phys Lett,2008.464(1-3):92-96.
[13]Zhang Y,Xie Y F,Song F R,et al.Quantitative Analysis of Berberine in Processed Coptis by Near-Infrared Diffuse Reflectance Spectroscopy[J].Chemical Research in Chinese Universities,2008.24(6):717-721.
[14]Homer J,Generalis S C,Robson J H.Artificial neural networks for the prediction of liquid viscosity, density, heat of vaporization, boiling point and Pitzer's acentric factor. Part I. Hydrocarbons[J].Phys Chem Chem Phys,1999.1(17):4075-4081.
[15]De Souza L E S,Canuto S.Efficient estimation of second virial coefficients of fused hard-sphere molecules by an artificial neural network[J].Phys Chem Chem Phys,2001.3(21):4762-4768.
[16]Duan X M,Li Z H,Song G L,et al.Neural network correction for heats of formation with a larger experimental training set and new descriptors[J].Chem Phys Lett,2005.410(1-3):125-130.
[17]Hu L H,Wang X J,Wong L H,et al.Combined first-principles calculation and neural-network correction approach for heat of formation[J].J Chem Phys,2003.119(22):11501-11507.
[18]Hu L H,Wang X J,Wong L H,et al.A combined first-principles calculation and neuralnetworks correction approach for evaluating Gibbs energy of formation[J].Molecular Simulation,2004.30(1):9-15.
[19]Alexandridis A,Patrinos P,Sarimveis H,et al.A two-stage evolutionary algorithm for variable selection in the development of RBF neural network models[J].Chemometrics and Intelligent Laboratory Systems,2005.75(2):149-162.
[20]Arakawa M,Hasegawa K,Funatsu K.QSAR study of anti-HIV HEPT analogues based on multi-objective genetic programming and counter-propagation neural network[J].Chemometrics and Intelligent Laboratory Systems,2006.83(2):91-98.
[21]Fernandez M,Caballero J.Ensembles of Bayesian-regularized Genetic Neural Networks for modeling of acetylcholinesterase inhibition by huprines[J].Chemical Biology & Drug Design,2006.68(4):201-212.
[22]Galvao R K H,Araujo M C U,Martins M D,et al.An application of subagging for the improvement of prediction accuracy of multivariate calibration models[J].Chemometrics and Intelligent Laboratory Systems,2006.81(1):60-67.
[23]Hemmateenejad B.Correlation ranking procedure for factor selection in PC-ANN modeling and application to ADMETox evaluation[J].Chemometrics and Intelligent Laboratory Systems,2005.75(2):231-245.
[24]Kim B,Kim S.GA-optimized backpropagation neural network with multi-parameterized gradients and applications to predicting plasma etch data[J] . Chemometrics and Intelligent Laboratory Systems,2005.79(1-2):123-128.
[25]Lee K T,Bhatia S,Mohamed A R,et al.Optimizing the specific surface area of fly ash-based sorbents for flue gas desulfurization[J].Chemosphere,2006.62(1):89-96.
[26]Sattari M,Gharagheizi F.Prediction of molecular diffusivity of pure components into air: A QSPR approach[J].Chemosphere,2008.72(9):1298-1302.
[27]Votano J R,Parham M,Hall L H,et al.Prediction of aqueous solubility based on large datasets using several QSPR models utilizing topological structure representation[J].Chemistry & Biodiversity,2004.1(11):1829-1841.
[28]Zhou P,Tian F F,Li Z L.A structure-based, quantitative structure-activity relationship approach for predicting HLA-A*0201-restricted cytotoxic T lymphocyte epitopes[J].Chemical Biology & Drug Design,2007.69(1):56-67.
[29]Wu J M,Xu X.The X1 method for accurate and efficient prediction of heats of formation[J].J Chem Phys,2007.127(21):214105-.
[30]Jianming Wu X X.Accurate prediction of heats of formation by a combined method of B3LYP and neural network correction[J].J Comput Chem,2008.9999(9999):NA.
[31]Wu J M,Xu X.Improving the B3LYP bond energies by using the X1 method[J].J Chem Phys,2008.129(16).
[32]Wodrich M D,Corminboeuf C.Reaction Enthalpies Using the Neural-Network-Based X1 Approach: The Important Choice of Input Descriptors[J].J Phys Chem A,2009.113(13):3285-3290.
[33]Gao T,Shi L L,Li H B,et al.Improving the accuracy of low level quantum chemical calculation for absorption energies: the genetic algorithm and neural network approach[J].Phys Chem Chem Phys,2009.DOI: 10.1039/b812492b.
[34]Vapnik V N.Statistical Learning Theory[M].New York:John Wiley and Sons,1998.
[35]Vapnik V.The Support Vector method of function estimation[J].Nonlinear Modeling,1998:55-85.
[36]刘焕香.基于支持向量机方法的QSAR/QSPR在化学、生物及环境科学中的应用研究[D]:[博士学位论文].兰州:兰州大学,(2005).
[37]栾峰.支持向量机(SVM)和径向基神经网络(RBFNN)方法在化学、环境科学和药物化学中的应用研究[D]:[博士学位论文].兰州:兰州大学,(2006).
[38]Feng L,Zhang X Y,Zhang H X,et al.Prediction of standard Gibbs energies of the transfer of peptide anions from aqueous solution to nitrobenzene based on support vector machineand the heuristic method[J].Journal of Computer-Aided Molecular Design,2006.20(1):1-11.
[39]Lind P , Maltseva T . Support vector machines for the estimation of aqueous solubility[J].Journal of Chemical Information and Computer Sciences,2003.43(6):1855-1859.
[40]Liu H M,Zhang R S,Yao X J,et al.Prediction of electrophoretic mobility of substituted aromatic acids in different aqueous-alcoholic solvents by capillary zone electrophoresis based on support vector machine[J].Analytica Chimica Acta,2004.525(1):31-41.
[41]Liu H X,Xue C X,Zhang R S,et al.Quantitative prediction of logk of peptides in high-performance liquid chromatography based on molecular descriptors by using the heuristic method and support vector machine[J].Journal of Chemical Information and Computer Sciences,2004.44(6):1979-1986.
[42]Liu H X,Yao X J,Xue C X,et al.Study of quantitative structure-mobility relationship of the peptides based on the structural descriptors and support vector machines[J].Analytica Chimica Acta,2005.542(2):249-259.
[43]Liu H X,Zhang R S,Yao X J,et al.Prediction of the isoelectric point of an amino acid based on GA-PLS and SVMs[J].Journal of Chemical Information and Computer Sciences,2004.44(1):161-167.
[44]Luan F,Xue C X,Zhang R S,et al.Prediction of retention time of a variety of volatile organic compounds based on the heuristic method and support vector machine[J].Analytica Chimica Acta,2005.537(1-2):101-110.
[45]Luan F,Zhang X Y,Zhang H X,et al.QSPR study of permeability coefficients through low-density polyethylene based on radial basis function neural networks and the heuristic method[J].Computational Materials Science,2006.37(4):454-461.
[46]Ma W P,Luan F,Zhang H X,et al.Quantitative structure-property relationships for pesticides in biopartitioning micellar chromatography[J].Journal of Chromatography A,2006.1113(1-2):140-147.
[47]Ma W P,Zhang X Y,Luan F,et al.Support vector machine and the heuristic method to predict the solubility of hydrocarbons in electrolyte[J].J Phys Chem A,2005.109(15):3485-3492.
[48]Serra J M,Baumes L A,Moliner M,et al.Zeolite synthesis modelling with support vector machines: A combinatorial approach[J].Combinatorial Chemistry & High Throughput Screening,2007.10(1):13-24.
[49]Xue C X,Liu H X,Yao X J,et al.Study of quantitative structure-mobility relationship of carboxylic and sulphonic acids in capillary electrophoresis[J] . Journal of Chromatography A,2004.1048(2):233-243.
[50]Xue C X,Zhang R S,Liu H X,et al.An accurate QSPR study of O-H bond dissociation energy in substituted phenols based on support vector machines[J].Journal of Chemical Information and Computer Sciences,2004.44(2):669-677.
[51]Xue C X,Zhang R S,Liu M C,et al.Study of the quantitative structure-mobility relationship of carboxylic acids in capillary electrophoresis based on support vector machines[J].Journal of Chemical Information and Computer Sciences,2004.44(3):950-957.
[52]Zheng G,Xiao M,Lu X H.Quantitative structure-activity relationships study on the Ah receptor binding affinities of Polybrominated Diphenyl Ethers using a support vector machine[J].Qsar Comb Sci,2007.26(4):536-541.
[53]Zhu X R,Li N,Shi X Y,et al.Study on the Chinese medicinal Qingkailing injections intermediate by support vector machines and ultraviolet spectrometry[J].Spectroscopy and Spectral Analysis,2008.28(7):1626-1629.
[54]Song M H,Breneman C M,Bi J B,et al.Prediction of protein retention times in anion-exchange chromatography systems using support vector regression[J].Journal ofChemical Information and Computer Sciences,2002.42(6):1347-1357.
[55]Song M H,Bi J B,Breneman C M,et al.Prediction of protein retention times in anion exchange chromatography systems using support vector machine regression[J].Abstracts of Papers of the American Chemical Society,2002.224:254-COMP.
[56]Breneman C M,Song M H,Bi J B,et al.Prediction of protein retention times in anion-exchange chromatography systems using support vector regression[C]. in Symposium on Chemometrics and Chemoinformatics held at the 224th American-Chemical-Society National Meeting.2004.Boston, MA.
[57]Liu Y.A comparative study on feature selection methods for drug discovery[J].Journal of Chemical Information and Computer Sciences,2004.44(5):1823-1828.
[58]Sorich M J,Miners J O,McKinnon R A,et al.Comparison of linear and nonlinear classification algorithms for the prediction of drug and chemical metabolism by human UDP-glucuronosyltransferase isoforms[J].Journal of Chemical Information and Computer Sciences,2003.43(6):2019-2024.
[59]Byvatov E,Fechner U,Sadowski J,et al.Comparison of support vector machine and artificial neural network systems for drug/nondrug classification[J].Journal of Chemical Information and Computer Sciences,2003.43(6):1882-1889.
[60]Sadik O,Land W H,Wanekaya A K,et al.Detection and classification of organophosphate nerve agent simulants using support vector machines with multiarray sensors[J].Journal of Chemical Information and Computer Sciences,2004.44(2):499-507.
[61]Zernov V V,Balakin K V,Ivaschenko A A,et al.Drug discovery using support vector machines. The case studies of drug-likeness, agrochemical-likeness, and enzyme inhibition predictions[J].Journal of Chemical Information and Computer Sciences,2003.43(6):2048-2056.
[62]Xue Y,Li Z R,Yap C W,et al.Effect of molecular descriptor feature selection in support vector machine classification of pharmacokinetic and toxicological properties of chemical agents[J].Journal of Chemical Information and Computer Sciences,2004.44(5):1630-1638.
[63]Czerminski R , Yasri A , Hartsough D . Use of Support Vector Machine in pattern classification: Application to QSAR studies[J] . Quantitative Structure-Activity Relationships,2001.20(3):227-240.
[64]Kovatcheva A,Golbraikh A,Oloff S,et al.Combinatorial QSAR of ambergris fragrance compounds[J].Journal of Chemical Information and Computer Sciences,2004.44(2):582-595.
[65]Yao X J,Panaye A,Doucet J P,et al.Comparative study of QSAR/QSPR correlations using support vector machines, radial basis function neural networks, and multiple linear regression[J].Journal of Chemical Information and Computer Sciences,2004.44(4):1257-1266.
[66]Burbidge R,Trotter M,Buxton B,et al.Drug design by machine learning: support vector machines for pharmaceutical data analysis[C]. in Symposium on Artificial Intelligence in Bioinformatic.2000.Birmingham, England.
[67]Kramer S,Frank E,Helma C.Fragment generation and support vector machines for inducing SARs[J].Sar and Qsar in Environmental Research,2002.13(5):509-523.
[68]Zhao C Y,Zhang H X,Zhang X Y,et al.Prediction of milk/plasma drug concentration (MIP) ratio using support vector machine (SVM) method[J].Pharmaceutical Research,2006.23(1):41-48.
[69]Xue C X,Zhang R S,Liu H X,et al.QSAR models for the prediction of binding affinities to human serum albumin using the heuristic method and a support vector machine[J].Journal of Chemical Information and Computer Sciences,2004.44(5):1693-1700.
[70]Liu H X , Zhang R S , Yao X J , et al . QSAR study of ethyl 2-[(3-methyl-2,5-dioxo(3-pyrrolinyl))amino]-4-(trifluoromethyl)pyrimidine-5-carboxylate: An inhibitor of AP-I and NF-kappa B mediated gene expression based on support vector machines[J].Journal of Chemical Information and Computer Sciences,2003.43(4):1288-1296.
[71]Norinder U.Support vector machine models in drug design: applications to drug transport processes and QSAR using simplex optimisations and variable selection[J].Neurocomputing,2003.55(1-2):337-346.
[72]Luan F,Zhang R S,Zhao C Y,et al.Classification of the carcinogenicity of N-nitroso compounds based on support vector machines and linear discriminant analysis[J].Chemical Research in Toxicology,2005.18(2):198-203.
[73]Luan F,Ma W P,Zhang X Y,et al.QSAR study of polychlorinated dibenzodioxins, dibenzofurans, and Biphenyls using the heuristic method and support vector machine[J].Qsar Comb Sci,2006.25(1):46-55.
[74]Luan F,Ma W P,Zhang X Y,et al.Quantitative structure-activity relationship models for prediction of sensory irritants (logRD(50)) of volatile organic chemicals[J].Chemosphere,2006.63(7):1142-1153.
[75]Maretto D A,Mello C,Poppi R J.Least-squares support vector machines to correct temperature-induced spectral variation in multivariate calibration[C]. in 13th International Conference on Near Infrared Spectroscopy.2007.Umea, SWEDEN.
[76]Thissen U,Ustun B,Melssen W J,et al.Multivariate calibration with least-squares support vector machines[J].Analytical Chemistry,2004.76(11):3099-3105.
[77]J.Turro N.现代分子光化学[M].姚绍明等译.北京:科学出版社,1987.
[78]张建成,王夺元.现代光化学[M].北京:化学工业出版社,2006.
[79]Klan P,Wirz J.Photochemistry of Organic Compounds: From Concepts to Practice[M],2009.
[80]Valeur B.Molecular Fluorescence: Principles and Applications[M]:Wiley-VCH Verlag GmbH,2001.
[81]Hutchison G R,Ratner M A,Marks T J.Accurate prediction of band gaps in neutral heterocyclic conjugated polymers[J].J Phys Chem A,2002.106(44):10596-10605.
[82]徐光宪,黎乐民.量子化学基本原理和从头计算法(上册) [M].北京:科学出版社,1981.
[83]Born M.Oppenheimer J R. Zur quantentheorie der molekeln[J].Annalen der Physik,1927.band84(20):457-484.
[84]Born M,Huang K.Dynamical Theory of Crystal Lattices[M].New York:Oxford University Press,1954.
[85]Hartree D R.Proc Camb Phil Soc,1928.24(111):89.
[86]Fock V.Annalen der Physik,1930(61):126.
[87]Hartree D.Calculations of Atomic Structure [M].Wiley,1957.
[88]Roothaan C C J.New developments in molecular orbital theory[J].Rev Mod Phy,1951.23(2):69-89.
[89]John C G.The Transactional Interpretation of Quantum Mechanics [M].News York: Department of Physics University of Washington,1986.
[90]Hoffmann R.An extended Hückel theory I. Hydrocarbons [J].J Chem Phys,1963.39:1397-1412.
[91]Pople A J.Segal G A. Approximate self-consistent molecular orbital theory.Ⅱ. Calculation with complete neglect of differential overlap[J].J Cheml Phys,1965. 43:S136-S151..
[92]Pople J A,Beveridge D L.近似分子轨道理论方法[M].江元生译.北京:科学出版社,1976.
[93]Ridley J E,Zerner M C.Triplet states via intermediate neglect of differential overlap: benzene, pyridine and the diazines[J].Theoretical Chem Acta,1976.42:223-236.
[94]Stewart J J p.Optimization of Parameters for Semi-Empirical Methods I-Method[J].J Comput Chem,1989.10:209-220.
[95]杨国春.有机非线性光学材料的TDDFT-SOS理论研究[D]:[博士学位论文].长春:东北师范大学,(2007).
[96]Stratmann R E,Scuseria G E,Frisch M J.An efficient implementation of time-dependent density-functional theory for the calculation of excitation energies of large molecules[J].J Chem Phys,1998.109(19):8218-8224.
[97]Koch W , Holthausen M C . A Chemist’s Guide to Density Functional Theory[M]. Weinheim,Germany:Wiley-VCH,2000.
[98]Hohenberg P,Kohn W.Inhomogeneous Electron Gas[J].Phys Rev,1964.136:B864-871.
[99]Petersilka M,Gossmann U J,Gross E K U.Excitation energies from time-dependent density-functional theory[J].Phys Rev Lett,1996.76(8):1212-1215.
[100]百度百科.基组[EB/OL].http://baike.baidu.com/view/1084454.htm,2009-04-21.
[101]飞思科技产品研发中心编著.神经网络理论与MATLAB 7实现[M],2005.
[102]蒋宗礼.人工神经网络导论[M].北京:高等教育出版社,2001.
[103]百度百科.支持向量机[EB/OL].http://baike.baidu.com/view/541845.htm,2009-04-15.
[104]CMSVM支持向量机中文建模平台.支持向量机方法简介[EB /OL].http://stream1.cma.gov.cn/cmsvm/svm.asp,2009-05-21.
[105]陈跃华.基于智能方法的熔融碳酸盐燃料电池/微型燃气轮机联合发电系统的建模与控制研究[D]:[博士学位论文].上海:上海交通大学,(2006).
[106]Suykens J A K,Vandewalle J,De Moor B.Optimal control by least squares support vector machines[J].Neural Networks,2001.14(1):23-35.
[107]Chauchard F,Cogdill R,Roussel S,et al.Application of LS-SVM to non-linear phenomena in NIR spectroscopy: development of a robust and portable sensor for acidity prediction in grapes[J].Chemometrics and Intelligent Laboratory Systems,2004.71(2):141-150.
[108]Liu H X,Yao X J,Zhang R S,et al.Prediction of the tissue/blood partition coefficients of organic compounds based on the molecular structure using least-squares support vector machines[J].Journal of Computer-Aided Molecular Design,2005.19(7):499-508.
[109]Patil N S,Shelokar P S,Jayaraman V K,et al.Regression models using pattern search assisted least square support vector machines[J].Chemical Engineering Research & Design,2005.83(A8):1030-1037.
[110]Yao X J,Liu H X,Zhang R S,et al.QSAR and classification study of 1,4-dihydropyridine calcium channel antagonists based on least squares support vector machines[J].Molecular Pharmaceutics,2005.2(5):348-356.
[111]Chauchard F,Svensson J,Axelsson J,et al.Localization of embedded inclusions using detection of fluorescence: Feasibility study based on simulation data, LS-SVM modeling and EPO pre-processing[C]. in 9th Annual Chemometrics Congress.2006.Paris, FRANCE.
[112]Li H Z,Liu H X,Yao X J,et al.Quantitative structure-activity relationship study of acyl ureas as inhibitors of human liver glycogen phosphorylase using least squares support vector machines[J] . Chemometrics and Intelligent Laboratory Systems ,2007.87(2):139-146.
[113]Wu D,He Y,Feng S J,et al.Study on infrared spectroscopy technique for fast measurement of protein content in milk powder based on LS-SVM[J].Journal of Food Engineering,2008.84(1):124-131.
[114]Zhu D Z,Ji B P,Meng C Y,et al.The application of direct orthogonal signal correction for linear and non-linear multivariate calibration[J].Chemometrics and Intelligent Laboratory Systems,2008.90(2):108-115.
[115]Exarchos K P,Papaloukas C,Exarchos T P,et al.Prediction of cis/trans isomerization using feature selection and support vector machines[J] . Journal of Biomedical Informatics,2009.42(1):140-149.
[116]Tao S H,Chen D Z,Zhao W X.Fast pruning algorithm for multi-output LS-SVM and its application in chemical pattern classification[J].Chemometrics and Intelligent Laboratory Systems,2009.96(1):63-69.
[117]Gao T,Sun S L,Shi L L,et al.An accurate density functional theory calculation forelectronic excitation energies: The least-squares support vector machine[J].J Chem Phys,2009.130(18):184104.
[118]Sundholm D.A density-functional-theory study of bacteriochlorophyll b[J].Phys Chem Chem Phys,2003.5(19):4265-4271.
[119]Hummel P,Oxgaard J,Goddard W A,et al.Ligand-Field Excited States of Metal Hexacarbonyls[J].Inorg Chem,2005.44(7):2454-2458.
[120]He R X,Duan X H,Li X Y.Quantum Chemical Study on Excited States and Electronic Coupling Matrix Element in a Catechol-Bridge-Dicyanoethylene System[J].J Phys Chem A,2005.109(18):4154-4161.
[121]Von Helden G,Compagnon I,Blom M N,et al.Mid-IR spectra of different conformers of phenylalanine in the gas phase[J].Phys Chem Chem Phys,2008.10(9):1248-1256.
[122]Schreiner P R,Fokin A A,Pascal R A,et al.Many density functional theory approaches fail to give reliable large hydrocarbon isomer energy differences[J].Organic Letters,2006.8(17):3635-3638.
[123]Wodrich M D,Corminboeuf C,Schleyer P V.Systematic errors in computed alkane energies using B3LYP and other popular DFT functionals[J].Organic Letters,2006.8(17):3631-3634.
[124]Pople J A,Headgordon M,Fox D J,et al.GAUSSIAN-1 THEORY - A GENERAL PROCEDURE FOR PREDICTION OF MOLECULAR-ENERGIES[J].J Chem Phys,1989.90(10):5622-5629.
[125]Curtiss L A,Raghavachari K,Trucks G W,et al.GAUSSIAN-2 THEORY FOR MOLECULAR-ENERGIES OF 1ST-ROW AND 2ND-ROW COMPOUNDS[J].J Chem Phys,1991.94(11):7221-7230.
[126]Curtiss L A,Raghavachari K,Redfern P C,et al.Gaussian-3 (G3) theory for molecules containing first and second-row atoms[J].J Chem Phys,1998.109(18):7764-7776.
[127]Curtiss L A,Redfern P C,Raghavachari K.Gaussian-4 theory[J].J Chem Phys,2007.126(8):084108.
[128]Zhang Y,Yang W.A challenge for density functionals: Self-interaction error increases for systems with a noninteger number of electrons[J].J Chem Phys,1998.109(7):2604-2608.
[129]Guyon I,Albrecht P,Le Cun Y,et al.Design of a neural network character recognizer for a touch terminal[J].Pattern Recogn,1991.24(2):105-119.
[130]Juell P,Marsh R.A hierarchical neural network for human face detection[J].Pattern Recogn,1996.29(5):781-787.
[131]Donaldson N D,Gollee H,Hunt K J,et al.A radial basis function model of muscle stimulated with irregular inter-pulse intervals[J].Medical Engineering & Physics,1995.17(6):431-441.
[132]Hunt K J,Sbarbaro D,Zbikowski R,et al.Neural networks for control systems--A survey[J].Automatica,1992.28(6):1083-1112.
[133]Luttrell S P.Image compression using a multilayer neural network[J].Pattern Recogn Lett,1989.10(1):1-7.
[134]Park S B,Lee J W,Kim S K.Content-based image classification using a neural network[J].Pattern Recogn Lett,2004.25(3):287-300.
[135]Gardner M W,Dorling S R.Statistical surface ozone models: an improved methodology to account for non-linear behaviour[J].Atmospheric Environment,2000.34(1):21-34.
[136]Yi J,Prybutok V R.A neural network model forecasting for prediction of daily maximum ozone concentration in an industrialized urban area[J].Environmental Pollution,1996.92(3):349-357.
[137]Kuo R J,Chen J A.A decision support system for order selection in electronic commerce based on fuzzy neural network supported by real-coded genetic algorithm[J].Expert Syst App,2004.26(2):141-154.
[138]Vellido A,Lisboa P J G,Meehan K.Segmentation of the on-line shopping market using neural networks[J].Expert Syst App,1999.17(4):303-314.
[139]French M N,Krajewski W F,Cuykendall R R.Rainfall forecasting in space and time using a neural network[J].Journal of Hydrology,1992.137(1-4):1-31.
[140]Makarynskyy O.Improving wave predictions with artificial neural networks[J].Ocean Engineering,2004.31(5-6):709-724.
[141]Li H,Shi L L,Zhang M,et al.Improving the accuracy of density-functional theory calculation: The genetic algorithm and neural network approach[J].J Chem Phys,2007.126(14):144101-.
[142]Loiseau F,Campagna S,Hameurlaine A,et al.Dendrimers Made of Porphyrin Cores and Carbazole Chromophores as Peripheral Units. Absorption Spectra, Luminescence Properties, and Oxidation Behavior[J].J Am Chem Soc.,2005.127(32):11352-11363.
[143]Li H,Shi L L,Zhang M,et al.Improving the accuracy of density-functional theory calculation: The genetic algorithm and neural network approach[J].J. Chem. Phys.,2007.126(14):144101.
[144]Lee C,Yang W,Parr R G.Development of the Colle-Salvetti correlation-energy formula into a functional of the electron density[J].Phys Rev B,1988.37(2):785-789.
[145]Becke A D.Density-functional thermochemistry. III. The role of exact exchange[J].J Chem Phys,1993.98(7):5648-5652.
[146]Stephens P J,Devlin F J,Chabalowski C F,et al.Ab Initio Calculation of Vibrational Absorption and Circular Dichroism Spectra Using Density Functional Force Fields[J].J Phys Chem,1994.98(45):11623-11627.
[147]Duan X M,Song G L,Li Z H,et al.Accurate prediction of heat of formation by combining Hartree-Fock/density functional theory calculation with linear regression correction approach[J].J Chem Phys,2004.121(15):7086-7095.
[148]Duan X M,Li Z H,Hu H R,et al.Linear regression correction to first principle theoretical calculations - Improved descriptors and enlarged training set[J].Chem Phys Lett,2005.409(4-6):315-321.
[149]Burges C J C.A tutorial on support vector machines for pattern recognition[J].Data Mining and Knowledge Discovery,1998.2(2):121-167.
[150]Pontil M,Verri A.Support vector machines for 3D object recognition[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1998.20(6):637-646.
[151]Burbidge R,Trotter M,Buxton B,et al.Drug design by machine learning: Support vector machines for pharmaceutical data analysis[J].Computers and Chemistry,2001.26(1):5-14.
[152]Guyon I,Weston J,Barnhill S,et al.Gene selection for cancer classification using support vector machines[J].Mach Learn,2002.46(1-3):389-422.
[153]Chapelle O,Haffner P,Vapnik V N.Support vector machines for histogram-based image classification[J].IEEE T Neural Networ,1999.10(5):1055-1064.
[154]Fukuda S,Hirosawa H.Polarimetric SAR image classification using support vector machines[J].Ieice Transactions on Electronics,2001.E84C(12):1939-1945.
[155]Roli F , Fumera G . Support vector machines for remote-sensing image classification[J].Image and Signal Processing for Remote Sensing Vi,2001.4170:160-166.
[156]Wang Y M,Zhang H J.Content-based image orientation detection with support vector machines[J].Ieee Workshop on Content-Based Access of Image and Video Libraries, Proceedings,2001:17-23.
[157]Foody G M,Mathur A.A relative evaluation of multiclass image classification by support vector machines[J].Ieee Transactions on Geoscience and Remote Sensing,2004.42(6):1335-1343.
[158]Gondra I,Heisterkamp D R,Peng J.Improving image retrieval performance by inter-query learning with one-class support vector machines[J].Neural Computing & Applications,2004.13(2):130-139.
[159]Lin T C,Yu P T.Adaptive two-pass median filter based on support vector machines for image restoration[J].Neural Computation,2004.16(2):333-354.
[160]Liu J J,Bharati M H,Dunn K G,et al.Automatic masking in multivariate image analysis using support vector machines[J].Chemometrics and Intelligent Laboratory Systems,2005.79(1-2):42-54.
[161]Tsai C F.Training support vector machines based on stacked generalization for image classification[J].Neurocomputing,2005.64:497-503.
[162]Chang C C , Liao C T . An image coding scheme using SMVQ and support vector machines[J].Neurocomputing,2006.69(16-18):2327-2335.
[163]Tao D C,Tang X,Li X L,et al.Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2006.28(7):1088-1099.
[164]Kotsia I,Pitas I.Facial expression recognition in image sequences using geometric deformation features and support vector machines[J].Ieee Transactions on Image Processing,2007.16(1):172-187.
[165]Seo K K.An application of one-class support vector machines in content-based image retrieval[J].Expert Syst App,2007.33(2):491-498.
[166]Tsai H H , Sun D W . Color image watermark extraction based on support vector machines[J].Information Sciences,2007.177(2):550-569.
[167]Wong W T,Shih F Y,Liu J.Shape-based image retrieval using support vector machines, Fourier descriptors and self-organizing maps[J].Information Sciences,2007.177(8):1878-1891.
[168]Mumtaz A,Gilani S A M,Hameed K,et al.Enhancing performance of image retrieval systems using dual tree complex wavelet transform and support vector machines[J].Journal of Computing and Information Technology - CIT,2008:57-68.
[169]Wu R S,Chung W H.Ensemble one-class support vector machines for content-based image retrieval[J].Expert Syst App,2009.36(3):4451-4459.
[170]Yao C C,Yu P T,Hung R W.Extractive Support Vector Algorithm on Support Vector Machines for Image Restoration[J].Fundamenta Informaticae,2009.90(1-2):171-190.
[171]Van Gestel T,Suykens J A K,Baestaens D E,et al.Financial time series prediction using least squares support vector machines within the evidence framework[J].IEEE T Neural Networ,2001.12(4):809-821.
[172]Tay F E H,Cao L J.Modified support vector machines in financial time series forecasting[J].Neurocomputing,2002.48(1-4):847-861.
[173]Kim K J . Financial time series forecasting using support vector machines[J].Neurocomputing,2003.55(1-2):307-319.
[174]Mukherjee S,Osuna E,Girosi F.Nonlinear prediction of chaotic time series using support vector machines[J].Neural Networks for Signal Processing Vii,1997:511-520.
[175]Lee K L,Billings S A.Time series prediction using support vector machines, the orthogonal and the regularized orthogonal least-squares algorithms[J].International Journal of Systems Science,2002.33(10):811-821.
[176]Thiessen U,van Brakel R,de Weijer A P,et al.Using support vector machines for time series prediction[J].Chemometrics and Intelligent Laboratory Systems,2003.69(1-2):35-49.
[177]Cui W Z,Zhu C C,Bao W X,et al.Prediction of the chaotic time series using support vector machines[J].Acta Physica Sinica,2004.53(10):3303-3310.
[178]Ye M Y,Wang X D.Chaotic time series prediction using least squares support vector machines[J].Chinese Physics,2004.13(4):454-458.
[179]Cui W Z,Zhu C C,Bao W X,et al.Prediction of the chaotic time series using support vector machines for fuzzy rule-based modeling[J].Acta Physica Sinica,2005.54(7):3009-3018.
[180]Cheng J,Qian J S,Guo Y N.A distributed support vector machines architecture for chaotic time series prediction[J].Neural Information Processing, Pt 1, Proceedings,2006.4232:892-899.
[181]Liu H,Liu D,Deng L F.Chaotic time series prediction using fuzzy sigmoid kernel-based support vector machines[J].Chinese Physics,2006.15(6):1196-1200.
[182]Qian J S,Cheng J,Guo Y N.A novel multiple support vector machines architecture for chaotic time series prediction[J].Advances in Natural Computation, Pt 1,2006.4221:147-156.
[183]Qisong C,Xiaowei C,Yun W.The combining kernel principal component analysis with support vector machines for time series prediction model[J].2008 Second International Symposium on Intelligent Information Technology Application,2008:90-94.
[184]Leopold E,Kindermann J.Text categorization with support vector machines. How to represent texts in input space ?[J].Mach Learn,2002.46(1-3):423-444.
[185]Diaz I,Ranilla J,Montanes E,et al.Improving performance of text categorization by combining filtering and support vector machines[J].Journal of the American Society for Information Science and Technology,2004.55(7):579-592.
[186]Zhao H,Rong L-l.Classification algorithm based on combined support vector machines and its application in text categorization[J].Mini-Micro Systems,2005.26(10):1816-1820.
[187]Kyung-Soon L,Kageura K.Virtual relevant documents in text categorization with support vector machines[J].Information Processing & Management,2007.43(4):902-913.
[188]Lee K S,Kageura K.Virtual relevant documents in text categorization with support vector machines[J].Information Processing & Management,2007.43(4):902-913.
[189]Chen L,Huang J,Gong Z H.An anti-noise text categorization method based on support vector machines[J].Advances in Web Intelligence, Proceedings,2005.3528:272-278.
[190]Lin X D,Peng H,Liu B,et al.Support vector machines for text categorization in Chinese question classification[J] . 2006 IEEE/WIC/ACM International Conference on Web Intelligence, (WI 2006 Main Conference Proceedings),2006:334-337.
[191]Suykens J A K,Vandewalle J.Recurrent least squares support vector machines[J].Ieee Transactions on Circuits and Systems I-Fundamental Theory and Applications ,2000.47(7):1109-1114.
[192]Viaene S,Baesens B,Van Gestel T,et al.Knowledge discovery in a direct marketing case using least squares support vector machines[J] . International Journal of Intelligent Systems,2001.16(9):1023-1036.
[193]Van Gestel T,Suykens J A K,Lanckriet G,et al.Bayesian framework for least-squares support vector machine classifiers, Gaussian processes, and kernel Fisher discriminant analysis[J].Neural Computation,2002.14(5):1115-1147.
[194]Liu J H,Chan J P,Jiang S,et al.Online LS-SVM for function estimation and classification[J].Journal of University of Science and Technology Beijing,2003.10(5):73-77.
[195]Lu C,Van Gestel T,Suykens J A K,et al.Preoperative prediction of malignancy of ovarian tumors using least squares support vector machines[J].Artificial Intelligence in Medicine,2003.28(3):281-306.
[196]Tsujinishi D,Abe S.Fuzzy least squares support vector machines for multiclass problems[C]. in INNS/IEEE International Joint Conference on Neural Network (IJCNN 03).2003.Portland, Oregon.
[197]Cawley G C,Talbot N L C.Fast exact leave-one-out cross-validation of sparse least-squares support vector machines[J].Neural Networks,2004.17(10):1467-1475.
[198]van Gestel T,Suykens J A K,Baesens B,et al.Benchmarking least squares support vector machine classifiers[J].Mach Learn,2004.54(1):5-32.
[199]Acir N.Classification of ECG beats by using a fast least square support vector machineswith a dynamic programming feature selection algorithm[J] . Neural Computing & Applications,2005.14(4):299-309.
[200]Goethals I,Pelckmans K,Suykens J A K,et al.Subspace identification of Hammerstein systems using least squares support vector machines[J].Ieee Transactions on Automatic Control,2005.50(10):1509-1519.
[201]Borin A,Ferrao M F,Mello C,et al.Least-squares support vector machines and near infrared spectroscopy for quantification of common adulterants in powdered milk[J].Analytica Chimica Acta,2006.579(1):25-32.
[202]Tang E K,Suganthan P N,Yao X.Gene selection algorithms for microarray data based on least squares support vector machine[J].Bmc Bioinformatics,2006.7.
[203]Li J Z,Liu H X,Yao X J,et al.Structure-activity relationship study of oxindole-based inhibitors of cyclin-dependent kinases based on least-squares support vector machines[J].Analytica Chimica Acta,2007.581(2):333-342.
[204]Pelckmans K,Suykens J A K,Van Gestel T,et al.LS-SVMlab: A Matlab/C Toolbox for Least Squares Support Vector Machines[EB/OL].
[2]Wang X J,Wong L H,Hu L H,et al.Improving the accuracy of density-functional theory calculation: The statistical correction approach[J].J Phys Chem A,2004.108(40):8514-8525.
[3]段雪梅.分子能量和热力学函数的精确计算——统计的校正方法[D]:[博士学位论文].上海:复旦大学,(2005).
[4]Foresman J B,Frisch E.Exploring Chemistry with Electronic Structure Methos[M]:Gaussian Inc,1996.
[5]Ghouri M M,Singh S,Ramachandran B.Scaled density functional theory correlation functionals[J].Journal of Physical Chemistry A,2007.111(41):10390-10399.
[6]Jacquemin D,Wathelet V,Perpete E A.Ab initio investigation of the n ->pi* transitions in thiocarbonyl dyes[J].Journal of Physical Chemistry A,2006.110(29):9145-9152.
[7]Jacquemin D,Perpete E A,Vydrov O A,et al.Assessment of long-range corrected functionals performance for n ->pi(*) transitions in organic dyes[J].Journal of Chemical Physics,2007.127(9):6.
[8]Duan X M,Song G L,Li Z H,et al.Accurate prediction of heat of formation by combining Hartree-Fock/density functional theory calculation with linear regression correction approach[J].J. Chem. Phys.,2004.121(15):7086-7095.
[9]Duan X M,Li Z H,Hu H R,et al.Linear regression correction to first principle theoretical calculations - Improved descriptors and enlarged training set[J].Chem. Phys. Lett.,2005.409(4-6):315-321.
[10]Liu G Z,Mi Z T.Prediction of pulsation frequency of pulsing flow in trickle-bed reactors using artificial neural network[J].Chemical Engineering Science,2004.59(24):5787-5794.
[11]Qu N,Wang L H,Zhu M C,et al.Radial basis function networks combined with genetic algorithm applied to nondestructive determination of compound erythromycin ethylsuccinate powder[J].Chemometrics and Intelligent Laboratory Systems,2008.90(2):145-152.
[12]Xu W W,Liu X G,Luan S X,et al.An ab initio potential energy surface of the He+H-2(+) -> HeH++H reaction[J].Chem Phys Lett,2008.464(1-3):92-96.
[13]Zhang Y,Xie Y F,Song F R,et al.Quantitative Analysis of Berberine in Processed Coptis by Near-Infrared Diffuse Reflectance Spectroscopy[J].Chemical Research in Chinese Universities,2008.24(6):717-721.
[14]Homer J,Generalis S C,Robson J H.Artificial neural networks for the prediction of liquid viscosity, density, heat of vaporization, boiling point and Pitzer's acentric factor. Part I. Hydrocarbons[J].Phys Chem Chem Phys,1999.1(17):4075-4081.
[15]De Souza L E S,Canuto S.Efficient estimation of second virial coefficients of fused hard-sphere molecules by an artificial neural network[J].Phys Chem Chem Phys,2001.3(21):4762-4768.
[16]Duan X M,Li Z H,Song G L,et al.Neural network correction for heats of formation with a larger experimental training set and new descriptors[J].Chem Phys Lett,2005.410(1-3):125-130.
[17]Hu L H,Wang X J,Wong L H,et al.Combined first-principles calculation and neural-network correction approach for heat of formation[J].J Chem Phys,2003.119(22):11501-11507.
[18]Hu L H,Wang X J,Wong L H,et al.A combined first-principles calculation and neuralnetworks correction approach for evaluating Gibbs energy of formation[J].Molecular Simulation,2004.30(1):9-15.
[19]Alexandridis A,Patrinos P,Sarimveis H,et al.A two-stage evolutionary algorithm for variable selection in the development of RBF neural network models[J].Chemometrics and Intelligent Laboratory Systems,2005.75(2):149-162.
[20]Arakawa M,Hasegawa K,Funatsu K.QSAR study of anti-HIV HEPT analogues based on multi-objective genetic programming and counter-propagation neural network[J].Chemometrics and Intelligent Laboratory Systems,2006.83(2):91-98.
[21]Fernandez M,Caballero J.Ensembles of Bayesian-regularized Genetic Neural Networks for modeling of acetylcholinesterase inhibition by huprines[J].Chemical Biology & Drug Design,2006.68(4):201-212.
[22]Galvao R K H,Araujo M C U,Martins M D,et al.An application of subagging for the improvement of prediction accuracy of multivariate calibration models[J].Chemometrics and Intelligent Laboratory Systems,2006.81(1):60-67.
[23]Hemmateenejad B.Correlation ranking procedure for factor selection in PC-ANN modeling and application to ADMETox evaluation[J].Chemometrics and Intelligent Laboratory Systems,2005.75(2):231-245.
[24]Kim B,Kim S.GA-optimized backpropagation neural network with multi-parameterized gradients and applications to predicting plasma etch data[J] . Chemometrics and Intelligent Laboratory Systems,2005.79(1-2):123-128.
[25]Lee K T,Bhatia S,Mohamed A R,et al.Optimizing the specific surface area of fly ash-based sorbents for flue gas desulfurization[J].Chemosphere,2006.62(1):89-96.
[26]Sattari M,Gharagheizi F.Prediction of molecular diffusivity of pure components into air: A QSPR approach[J].Chemosphere,2008.72(9):1298-1302.
[27]Votano J R,Parham M,Hall L H,et al.Prediction of aqueous solubility based on large datasets using several QSPR models utilizing topological structure representation[J].Chemistry & Biodiversity,2004.1(11):1829-1841.
[28]Zhou P,Tian F F,Li Z L.A structure-based, quantitative structure-activity relationship approach for predicting HLA-A*0201-restricted cytotoxic T lymphocyte epitopes[J].Chemical Biology & Drug Design,2007.69(1):56-67.
[29]Wu J M,Xu X.The X1 method for accurate and efficient prediction of heats of formation[J].J Chem Phys,2007.127(21):214105-.
[30]Jianming Wu X X.Accurate prediction of heats of formation by a combined method of B3LYP and neural network correction[J].J Comput Chem,2008.9999(9999):NA.
[31]Wu J M,Xu X.Improving the B3LYP bond energies by using the X1 method[J].J Chem Phys,2008.129(16).
[32]Wodrich M D,Corminboeuf C.Reaction Enthalpies Using the Neural-Network-Based X1 Approach: The Important Choice of Input Descriptors[J].J Phys Chem A,2009.113(13):3285-3290.
[33]Gao T,Shi L L,Li H B,et al.Improving the accuracy of low level quantum chemical calculation for absorption energies: the genetic algorithm and neural network approach[J].Phys Chem Chem Phys,2009.DOI: 10.1039/b812492b.
[34]Vapnik V N.Statistical Learning Theory[M].New York:John Wiley and Sons,1998.
[35]Vapnik V.The Support Vector method of function estimation[J].Nonlinear Modeling,1998:55-85.
[36]刘焕香.基于支持向量机方法的QSAR/QSPR在化学、生物及环境科学中的应用研究[D]:[博士学位论文].兰州:兰州大学,(2005).
[37]栾峰.支持向量机(SVM)和径向基神经网络(RBFNN)方法在化学、环境科学和药物化学中的应用研究[D]:[博士学位论文].兰州:兰州大学,(2006).
[38]Feng L,Zhang X Y,Zhang H X,et al.Prediction of standard Gibbs energies of the transfer of peptide anions from aqueous solution to nitrobenzene based on support vector machineand the heuristic method[J].Journal of Computer-Aided Molecular Design,2006.20(1):1-11.
[39]Lind P , Maltseva T . Support vector machines for the estimation of aqueous solubility[J].Journal of Chemical Information and Computer Sciences,2003.43(6):1855-1859.
[40]Liu H M,Zhang R S,Yao X J,et al.Prediction of electrophoretic mobility of substituted aromatic acids in different aqueous-alcoholic solvents by capillary zone electrophoresis based on support vector machine[J].Analytica Chimica Acta,2004.525(1):31-41.
[41]Liu H X,Xue C X,Zhang R S,et al.Quantitative prediction of logk of peptides in high-performance liquid chromatography based on molecular descriptors by using the heuristic method and support vector machine[J].Journal of Chemical Information and Computer Sciences,2004.44(6):1979-1986.
[42]Liu H X,Yao X J,Xue C X,et al.Study of quantitative structure-mobility relationship of the peptides based on the structural descriptors and support vector machines[J].Analytica Chimica Acta,2005.542(2):249-259.
[43]Liu H X,Zhang R S,Yao X J,et al.Prediction of the isoelectric point of an amino acid based on GA-PLS and SVMs[J].Journal of Chemical Information and Computer Sciences,2004.44(1):161-167.
[44]Luan F,Xue C X,Zhang R S,et al.Prediction of retention time of a variety of volatile organic compounds based on the heuristic method and support vector machine[J].Analytica Chimica Acta,2005.537(1-2):101-110.
[45]Luan F,Zhang X Y,Zhang H X,et al.QSPR study of permeability coefficients through low-density polyethylene based on radial basis function neural networks and the heuristic method[J].Computational Materials Science,2006.37(4):454-461.
[46]Ma W P,Luan F,Zhang H X,et al.Quantitative structure-property relationships for pesticides in biopartitioning micellar chromatography[J].Journal of Chromatography A,2006.1113(1-2):140-147.
[47]Ma W P,Zhang X Y,Luan F,et al.Support vector machine and the heuristic method to predict the solubility of hydrocarbons in electrolyte[J].J Phys Chem A,2005.109(15):3485-3492.
[48]Serra J M,Baumes L A,Moliner M,et al.Zeolite synthesis modelling with support vector machines: A combinatorial approach[J].Combinatorial Chemistry & High Throughput Screening,2007.10(1):13-24.
[49]Xue C X,Liu H X,Yao X J,et al.Study of quantitative structure-mobility relationship of carboxylic and sulphonic acids in capillary electrophoresis[J] . Journal of Chromatography A,2004.1048(2):233-243.
[50]Xue C X,Zhang R S,Liu H X,et al.An accurate QSPR study of O-H bond dissociation energy in substituted phenols based on support vector machines[J].Journal of Chemical Information and Computer Sciences,2004.44(2):669-677.
[51]Xue C X,Zhang R S,Liu M C,et al.Study of the quantitative structure-mobility relationship of carboxylic acids in capillary electrophoresis based on support vector machines[J].Journal of Chemical Information and Computer Sciences,2004.44(3):950-957.
[52]Zheng G,Xiao M,Lu X H.Quantitative structure-activity relationships study on the Ah receptor binding affinities of Polybrominated Diphenyl Ethers using a support vector machine[J].Qsar Comb Sci,2007.26(4):536-541.
[53]Zhu X R,Li N,Shi X Y,et al.Study on the Chinese medicinal Qingkailing injections intermediate by support vector machines and ultraviolet spectrometry[J].Spectroscopy and Spectral Analysis,2008.28(7):1626-1629.
[54]Song M H,Breneman C M,Bi J B,et al.Prediction of protein retention times in anion-exchange chromatography systems using support vector regression[J].Journal ofChemical Information and Computer Sciences,2002.42(6):1347-1357.
[55]Song M H,Bi J B,Breneman C M,et al.Prediction of protein retention times in anion exchange chromatography systems using support vector machine regression[J].Abstracts of Papers of the American Chemical Society,2002.224:254-COMP.
[56]Breneman C M,Song M H,Bi J B,et al.Prediction of protein retention times in anion-exchange chromatography systems using support vector regression[C]. in Symposium on Chemometrics and Chemoinformatics held at the 224th American-Chemical-Society National Meeting.2004.Boston, MA.
[57]Liu Y.A comparative study on feature selection methods for drug discovery[J].Journal of Chemical Information and Computer Sciences,2004.44(5):1823-1828.
[58]Sorich M J,Miners J O,McKinnon R A,et al.Comparison of linear and nonlinear classification algorithms for the prediction of drug and chemical metabolism by human UDP-glucuronosyltransferase isoforms[J].Journal of Chemical Information and Computer Sciences,2003.43(6):2019-2024.
[59]Byvatov E,Fechner U,Sadowski J,et al.Comparison of support vector machine and artificial neural network systems for drug/nondrug classification[J].Journal of Chemical Information and Computer Sciences,2003.43(6):1882-1889.
[60]Sadik O,Land W H,Wanekaya A K,et al.Detection and classification of organophosphate nerve agent simulants using support vector machines with multiarray sensors[J].Journal of Chemical Information and Computer Sciences,2004.44(2):499-507.
[61]Zernov V V,Balakin K V,Ivaschenko A A,et al.Drug discovery using support vector machines. The case studies of drug-likeness, agrochemical-likeness, and enzyme inhibition predictions[J].Journal of Chemical Information and Computer Sciences,2003.43(6):2048-2056.
[62]Xue Y,Li Z R,Yap C W,et al.Effect of molecular descriptor feature selection in support vector machine classification of pharmacokinetic and toxicological properties of chemical agents[J].Journal of Chemical Information and Computer Sciences,2004.44(5):1630-1638.
[63]Czerminski R , Yasri A , Hartsough D . Use of Support Vector Machine in pattern classification: Application to QSAR studies[J] . Quantitative Structure-Activity Relationships,2001.20(3):227-240.
[64]Kovatcheva A,Golbraikh A,Oloff S,et al.Combinatorial QSAR of ambergris fragrance compounds[J].Journal of Chemical Information and Computer Sciences,2004.44(2):582-595.
[65]Yao X J,Panaye A,Doucet J P,et al.Comparative study of QSAR/QSPR correlations using support vector machines, radial basis function neural networks, and multiple linear regression[J].Journal of Chemical Information and Computer Sciences,2004.44(4):1257-1266.
[66]Burbidge R,Trotter M,Buxton B,et al.Drug design by machine learning: support vector machines for pharmaceutical data analysis[C]. in Symposium on Artificial Intelligence in Bioinformatic.2000.Birmingham, England.
[67]Kramer S,Frank E,Helma C.Fragment generation and support vector machines for inducing SARs[J].Sar and Qsar in Environmental Research,2002.13(5):509-523.
[68]Zhao C Y,Zhang H X,Zhang X Y,et al.Prediction of milk/plasma drug concentration (MIP) ratio using support vector machine (SVM) method[J].Pharmaceutical Research,2006.23(1):41-48.
[69]Xue C X,Zhang R S,Liu H X,et al.QSAR models for the prediction of binding affinities to human serum albumin using the heuristic method and a support vector machine[J].Journal of Chemical Information and Computer Sciences,2004.44(5):1693-1700.
[70]Liu H X , Zhang R S , Yao X J , et al . QSAR study of ethyl 2-[(3-methyl-2,5-dioxo(3-pyrrolinyl))amino]-4-(trifluoromethyl)pyrimidine-5-carboxylate: An inhibitor of AP-I and NF-kappa B mediated gene expression based on support vector machines[J].Journal of Chemical Information and Computer Sciences,2003.43(4):1288-1296.
[71]Norinder U.Support vector machine models in drug design: applications to drug transport processes and QSAR using simplex optimisations and variable selection[J].Neurocomputing,2003.55(1-2):337-346.
[72]Luan F,Zhang R S,Zhao C Y,et al.Classification of the carcinogenicity of N-nitroso compounds based on support vector machines and linear discriminant analysis[J].Chemical Research in Toxicology,2005.18(2):198-203.
[73]Luan F,Ma W P,Zhang X Y,et al.QSAR study of polychlorinated dibenzodioxins, dibenzofurans, and Biphenyls using the heuristic method and support vector machine[J].Qsar Comb Sci,2006.25(1):46-55.
[74]Luan F,Ma W P,Zhang X Y,et al.Quantitative structure-activity relationship models for prediction of sensory irritants (logRD(50)) of volatile organic chemicals[J].Chemosphere,2006.63(7):1142-1153.
[75]Maretto D A,Mello C,Poppi R J.Least-squares support vector machines to correct temperature-induced spectral variation in multivariate calibration[C]. in 13th International Conference on Near Infrared Spectroscopy.2007.Umea, SWEDEN.
[76]Thissen U,Ustun B,Melssen W J,et al.Multivariate calibration with least-squares support vector machines[J].Analytical Chemistry,2004.76(11):3099-3105.
[77]J.Turro N.现代分子光化学[M].姚绍明等译.北京:科学出版社,1987.
[78]张建成,王夺元.现代光化学[M].北京:化学工业出版社,2006.
[79]Klan P,Wirz J.Photochemistry of Organic Compounds: From Concepts to Practice[M],2009.
[80]Valeur B.Molecular Fluorescence: Principles and Applications[M]:Wiley-VCH Verlag GmbH,2001.
[81]Hutchison G R,Ratner M A,Marks T J.Accurate prediction of band gaps in neutral heterocyclic conjugated polymers[J].J Phys Chem A,2002.106(44):10596-10605.
[82]徐光宪,黎乐民.量子化学基本原理和从头计算法(上册) [M].北京:科学出版社,1981.
[83]Born M.Oppenheimer J R. Zur quantentheorie der molekeln[J].Annalen der Physik,1927.band84(20):457-484.
[84]Born M,Huang K.Dynamical Theory of Crystal Lattices[M].New York:Oxford University Press,1954.
[85]Hartree D R.Proc Camb Phil Soc,1928.24(111):89.
[86]Fock V.Annalen der Physik,1930(61):126.
[87]Hartree D.Calculations of Atomic Structure [M].Wiley,1957.
[88]Roothaan C C J.New developments in molecular orbital theory[J].Rev Mod Phy,1951.23(2):69-89.
[89]John C G.The Transactional Interpretation of Quantum Mechanics [M].News York: Department of Physics University of Washington,1986.
[90]Hoffmann R.An extended Hückel theory I. Hydrocarbons [J].J Chem Phys,1963.39:1397-1412.
[91]Pople A J.Segal G A. Approximate self-consistent molecular orbital theory.Ⅱ. Calculation with complete neglect of differential overlap[J].J Cheml Phys,1965. 43:S136-S151..
[92]Pople J A,Beveridge D L.近似分子轨道理论方法[M].江元生译.北京:科学出版社,1976.
[93]Ridley J E,Zerner M C.Triplet states via intermediate neglect of differential overlap: benzene, pyridine and the diazines[J].Theoretical Chem Acta,1976.42:223-236.
[94]Stewart J J p.Optimization of Parameters for Semi-Empirical Methods I-Method[J].J Comput Chem,1989.10:209-220.
[95]杨国春.有机非线性光学材料的TDDFT-SOS理论研究[D]:[博士学位论文].长春:东北师范大学,(2007).
[96]Stratmann R E,Scuseria G E,Frisch M J.An efficient implementation of time-dependent density-functional theory for the calculation of excitation energies of large molecules[J].J Chem Phys,1998.109(19):8218-8224.
[97]Koch W , Holthausen M C . A Chemist’s Guide to Density Functional Theory[M]. Weinheim,Germany:Wiley-VCH,2000.
[98]Hohenberg P,Kohn W.Inhomogeneous Electron Gas[J].Phys Rev,1964.136:B864-871.
[99]Petersilka M,Gossmann U J,Gross E K U.Excitation energies from time-dependent density-functional theory[J].Phys Rev Lett,1996.76(8):1212-1215.
[100]百度百科.基组[EB/OL].http://baike.baidu.com/view/1084454.htm,2009-04-21.
[101]飞思科技产品研发中心编著.神经网络理论与MATLAB 7实现[M],2005.
[102]蒋宗礼.人工神经网络导论[M].北京:高等教育出版社,2001.
[103]百度百科.支持向量机[EB/OL].http://baike.baidu.com/view/541845.htm,2009-04-15.
[104]CMSVM支持向量机中文建模平台.支持向量机方法简介[EB /OL].http://stream1.cma.gov.cn/cmsvm/svm.asp,2009-05-21.
[105]陈跃华.基于智能方法的熔融碳酸盐燃料电池/微型燃气轮机联合发电系统的建模与控制研究[D]:[博士学位论文].上海:上海交通大学,(2006).
[106]Suykens J A K,Vandewalle J,De Moor B.Optimal control by least squares support vector machines[J].Neural Networks,2001.14(1):23-35.
[107]Chauchard F,Cogdill R,Roussel S,et al.Application of LS-SVM to non-linear phenomena in NIR spectroscopy: development of a robust and portable sensor for acidity prediction in grapes[J].Chemometrics and Intelligent Laboratory Systems,2004.71(2):141-150.
[108]Liu H X,Yao X J,Zhang R S,et al.Prediction of the tissue/blood partition coefficients of organic compounds based on the molecular structure using least-squares support vector machines[J].Journal of Computer-Aided Molecular Design,2005.19(7):499-508.
[109]Patil N S,Shelokar P S,Jayaraman V K,et al.Regression models using pattern search assisted least square support vector machines[J].Chemical Engineering Research & Design,2005.83(A8):1030-1037.
[110]Yao X J,Liu H X,Zhang R S,et al.QSAR and classification study of 1,4-dihydropyridine calcium channel antagonists based on least squares support vector machines[J].Molecular Pharmaceutics,2005.2(5):348-356.
[111]Chauchard F,Svensson J,Axelsson J,et al.Localization of embedded inclusions using detection of fluorescence: Feasibility study based on simulation data, LS-SVM modeling and EPO pre-processing[C]. in 9th Annual Chemometrics Congress.2006.Paris, FRANCE.
[112]Li H Z,Liu H X,Yao X J,et al.Quantitative structure-activity relationship study of acyl ureas as inhibitors of human liver glycogen phosphorylase using least squares support vector machines[J] . Chemometrics and Intelligent Laboratory Systems ,2007.87(2):139-146.
[113]Wu D,He Y,Feng S J,et al.Study on infrared spectroscopy technique for fast measurement of protein content in milk powder based on LS-SVM[J].Journal of Food Engineering,2008.84(1):124-131.
[114]Zhu D Z,Ji B P,Meng C Y,et al.The application of direct orthogonal signal correction for linear and non-linear multivariate calibration[J].Chemometrics and Intelligent Laboratory Systems,2008.90(2):108-115.
[115]Exarchos K P,Papaloukas C,Exarchos T P,et al.Prediction of cis/trans isomerization using feature selection and support vector machines[J] . Journal of Biomedical Informatics,2009.42(1):140-149.
[116]Tao S H,Chen D Z,Zhao W X.Fast pruning algorithm for multi-output LS-SVM and its application in chemical pattern classification[J].Chemometrics and Intelligent Laboratory Systems,2009.96(1):63-69.
[117]Gao T,Sun S L,Shi L L,et al.An accurate density functional theory calculation forelectronic excitation energies: The least-squares support vector machine[J].J Chem Phys,2009.130(18):184104.
[118]Sundholm D.A density-functional-theory study of bacteriochlorophyll b[J].Phys Chem Chem Phys,2003.5(19):4265-4271.
[119]Hummel P,Oxgaard J,Goddard W A,et al.Ligand-Field Excited States of Metal Hexacarbonyls[J].Inorg Chem,2005.44(7):2454-2458.
[120]He R X,Duan X H,Li X Y.Quantum Chemical Study on Excited States and Electronic Coupling Matrix Element in a Catechol-Bridge-Dicyanoethylene System[J].J Phys Chem A,2005.109(18):4154-4161.
[121]Von Helden G,Compagnon I,Blom M N,et al.Mid-IR spectra of different conformers of phenylalanine in the gas phase[J].Phys Chem Chem Phys,2008.10(9):1248-1256.
[122]Schreiner P R,Fokin A A,Pascal R A,et al.Many density functional theory approaches fail to give reliable large hydrocarbon isomer energy differences[J].Organic Letters,2006.8(17):3635-3638.
[123]Wodrich M D,Corminboeuf C,Schleyer P V.Systematic errors in computed alkane energies using B3LYP and other popular DFT functionals[J].Organic Letters,2006.8(17):3631-3634.
[124]Pople J A,Headgordon M,Fox D J,et al.GAUSSIAN-1 THEORY - A GENERAL PROCEDURE FOR PREDICTION OF MOLECULAR-ENERGIES[J].J Chem Phys,1989.90(10):5622-5629.
[125]Curtiss L A,Raghavachari K,Trucks G W,et al.GAUSSIAN-2 THEORY FOR MOLECULAR-ENERGIES OF 1ST-ROW AND 2ND-ROW COMPOUNDS[J].J Chem Phys,1991.94(11):7221-7230.
[126]Curtiss L A,Raghavachari K,Redfern P C,et al.Gaussian-3 (G3) theory for molecules containing first and second-row atoms[J].J Chem Phys,1998.109(18):7764-7776.
[127]Curtiss L A,Redfern P C,Raghavachari K.Gaussian-4 theory[J].J Chem Phys,2007.126(8):084108.
[128]Zhang Y,Yang W.A challenge for density functionals: Self-interaction error increases for systems with a noninteger number of electrons[J].J Chem Phys,1998.109(7):2604-2608.
[129]Guyon I,Albrecht P,Le Cun Y,et al.Design of a neural network character recognizer for a touch terminal[J].Pattern Recogn,1991.24(2):105-119.
[130]Juell P,Marsh R.A hierarchical neural network for human face detection[J].Pattern Recogn,1996.29(5):781-787.
[131]Donaldson N D,Gollee H,Hunt K J,et al.A radial basis function model of muscle stimulated with irregular inter-pulse intervals[J].Medical Engineering & Physics,1995.17(6):431-441.
[132]Hunt K J,Sbarbaro D,Zbikowski R,et al.Neural networks for control systems--A survey[J].Automatica,1992.28(6):1083-1112.
[133]Luttrell S P.Image compression using a multilayer neural network[J].Pattern Recogn Lett,1989.10(1):1-7.
[134]Park S B,Lee J W,Kim S K.Content-based image classification using a neural network[J].Pattern Recogn Lett,2004.25(3):287-300.
[135]Gardner M W,Dorling S R.Statistical surface ozone models: an improved methodology to account for non-linear behaviour[J].Atmospheric Environment,2000.34(1):21-34.
[136]Yi J,Prybutok V R.A neural network model forecasting for prediction of daily maximum ozone concentration in an industrialized urban area[J].Environmental Pollution,1996.92(3):349-357.
[137]Kuo R J,Chen J A.A decision support system for order selection in electronic commerce based on fuzzy neural network supported by real-coded genetic algorithm[J].Expert Syst App,2004.26(2):141-154.
[138]Vellido A,Lisboa P J G,Meehan K.Segmentation of the on-line shopping market using neural networks[J].Expert Syst App,1999.17(4):303-314.
[139]French M N,Krajewski W F,Cuykendall R R.Rainfall forecasting in space and time using a neural network[J].Journal of Hydrology,1992.137(1-4):1-31.
[140]Makarynskyy O.Improving wave predictions with artificial neural networks[J].Ocean Engineering,2004.31(5-6):709-724.
[141]Li H,Shi L L,Zhang M,et al.Improving the accuracy of density-functional theory calculation: The genetic algorithm and neural network approach[J].J Chem Phys,2007.126(14):144101-.
[142]Loiseau F,Campagna S,Hameurlaine A,et al.Dendrimers Made of Porphyrin Cores and Carbazole Chromophores as Peripheral Units. Absorption Spectra, Luminescence Properties, and Oxidation Behavior[J].J Am Chem Soc.,2005.127(32):11352-11363.
[143]Li H,Shi L L,Zhang M,et al.Improving the accuracy of density-functional theory calculation: The genetic algorithm and neural network approach[J].J. Chem. Phys.,2007.126(14):144101.
[144]Lee C,Yang W,Parr R G.Development of the Colle-Salvetti correlation-energy formula into a functional of the electron density[J].Phys Rev B,1988.37(2):785-789.
[145]Becke A D.Density-functional thermochemistry. III. The role of exact exchange[J].J Chem Phys,1993.98(7):5648-5652.
[146]Stephens P J,Devlin F J,Chabalowski C F,et al.Ab Initio Calculation of Vibrational Absorption and Circular Dichroism Spectra Using Density Functional Force Fields[J].J Phys Chem,1994.98(45):11623-11627.
[147]Duan X M,Song G L,Li Z H,et al.Accurate prediction of heat of formation by combining Hartree-Fock/density functional theory calculation with linear regression correction approach[J].J Chem Phys,2004.121(15):7086-7095.
[148]Duan X M,Li Z H,Hu H R,et al.Linear regression correction to first principle theoretical calculations - Improved descriptors and enlarged training set[J].Chem Phys Lett,2005.409(4-6):315-321.
[149]Burges C J C.A tutorial on support vector machines for pattern recognition[J].Data Mining and Knowledge Discovery,1998.2(2):121-167.
[150]Pontil M,Verri A.Support vector machines for 3D object recognition[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1998.20(6):637-646.
[151]Burbidge R,Trotter M,Buxton B,et al.Drug design by machine learning: Support vector machines for pharmaceutical data analysis[J].Computers and Chemistry,2001.26(1):5-14.
[152]Guyon I,Weston J,Barnhill S,et al.Gene selection for cancer classification using support vector machines[J].Mach Learn,2002.46(1-3):389-422.
[153]Chapelle O,Haffner P,Vapnik V N.Support vector machines for histogram-based image classification[J].IEEE T Neural Networ,1999.10(5):1055-1064.
[154]Fukuda S,Hirosawa H.Polarimetric SAR image classification using support vector machines[J].Ieice Transactions on Electronics,2001.E84C(12):1939-1945.
[155]Roli F , Fumera G . Support vector machines for remote-sensing image classification[J].Image and Signal Processing for Remote Sensing Vi,2001.4170:160-166.
[156]Wang Y M,Zhang H J.Content-based image orientation detection with support vector machines[J].Ieee Workshop on Content-Based Access of Image and Video Libraries, Proceedings,2001:17-23.
[157]Foody G M,Mathur A.A relative evaluation of multiclass image classification by support vector machines[J].Ieee Transactions on Geoscience and Remote Sensing,2004.42(6):1335-1343.
[158]Gondra I,Heisterkamp D R,Peng J.Improving image retrieval performance by inter-query learning with one-class support vector machines[J].Neural Computing & Applications,2004.13(2):130-139.
[159]Lin T C,Yu P T.Adaptive two-pass median filter based on support vector machines for image restoration[J].Neural Computation,2004.16(2):333-354.
[160]Liu J J,Bharati M H,Dunn K G,et al.Automatic masking in multivariate image analysis using support vector machines[J].Chemometrics and Intelligent Laboratory Systems,2005.79(1-2):42-54.
[161]Tsai C F.Training support vector machines based on stacked generalization for image classification[J].Neurocomputing,2005.64:497-503.
[162]Chang C C , Liao C T . An image coding scheme using SMVQ and support vector machines[J].Neurocomputing,2006.69(16-18):2327-2335.
[163]Tao D C,Tang X,Li X L,et al.Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2006.28(7):1088-1099.
[164]Kotsia I,Pitas I.Facial expression recognition in image sequences using geometric deformation features and support vector machines[J].Ieee Transactions on Image Processing,2007.16(1):172-187.
[165]Seo K K.An application of one-class support vector machines in content-based image retrieval[J].Expert Syst App,2007.33(2):491-498.
[166]Tsai H H , Sun D W . Color image watermark extraction based on support vector machines[J].Information Sciences,2007.177(2):550-569.
[167]Wong W T,Shih F Y,Liu J.Shape-based image retrieval using support vector machines, Fourier descriptors and self-organizing maps[J].Information Sciences,2007.177(8):1878-1891.
[168]Mumtaz A,Gilani S A M,Hameed K,et al.Enhancing performance of image retrieval systems using dual tree complex wavelet transform and support vector machines[J].Journal of Computing and Information Technology - CIT,2008:57-68.
[169]Wu R S,Chung W H.Ensemble one-class support vector machines for content-based image retrieval[J].Expert Syst App,2009.36(3):4451-4459.
[170]Yao C C,Yu P T,Hung R W.Extractive Support Vector Algorithm on Support Vector Machines for Image Restoration[J].Fundamenta Informaticae,2009.90(1-2):171-190.
[171]Van Gestel T,Suykens J A K,Baestaens D E,et al.Financial time series prediction using least squares support vector machines within the evidence framework[J].IEEE T Neural Networ,2001.12(4):809-821.
[172]Tay F E H,Cao L J.Modified support vector machines in financial time series forecasting[J].Neurocomputing,2002.48(1-4):847-861.
[173]Kim K J . Financial time series forecasting using support vector machines[J].Neurocomputing,2003.55(1-2):307-319.
[174]Mukherjee S,Osuna E,Girosi F.Nonlinear prediction of chaotic time series using support vector machines[J].Neural Networks for Signal Processing Vii,1997:511-520.
[175]Lee K L,Billings S A.Time series prediction using support vector machines, the orthogonal and the regularized orthogonal least-squares algorithms[J].International Journal of Systems Science,2002.33(10):811-821.
[176]Thiessen U,van Brakel R,de Weijer A P,et al.Using support vector machines for time series prediction[J].Chemometrics and Intelligent Laboratory Systems,2003.69(1-2):35-49.
[177]Cui W Z,Zhu C C,Bao W X,et al.Prediction of the chaotic time series using support vector machines[J].Acta Physica Sinica,2004.53(10):3303-3310.
[178]Ye M Y,Wang X D.Chaotic time series prediction using least squares support vector machines[J].Chinese Physics,2004.13(4):454-458.
[179]Cui W Z,Zhu C C,Bao W X,et al.Prediction of the chaotic time series using support vector machines for fuzzy rule-based modeling[J].Acta Physica Sinica,2005.54(7):3009-3018.
[180]Cheng J,Qian J S,Guo Y N.A distributed support vector machines architecture for chaotic time series prediction[J].Neural Information Processing, Pt 1, Proceedings,2006.4232:892-899.
[181]Liu H,Liu D,Deng L F.Chaotic time series prediction using fuzzy sigmoid kernel-based support vector machines[J].Chinese Physics,2006.15(6):1196-1200.
[182]Qian J S,Cheng J,Guo Y N.A novel multiple support vector machines architecture for chaotic time series prediction[J].Advances in Natural Computation, Pt 1,2006.4221:147-156.
[183]Qisong C,Xiaowei C,Yun W.The combining kernel principal component analysis with support vector machines for time series prediction model[J].2008 Second International Symposium on Intelligent Information Technology Application,2008:90-94.
[184]Leopold E,Kindermann J.Text categorization with support vector machines. How to represent texts in input space ?[J].Mach Learn,2002.46(1-3):423-444.
[185]Diaz I,Ranilla J,Montanes E,et al.Improving performance of text categorization by combining filtering and support vector machines[J].Journal of the American Society for Information Science and Technology,2004.55(7):579-592.
[186]Zhao H,Rong L-l.Classification algorithm based on combined support vector machines and its application in text categorization[J].Mini-Micro Systems,2005.26(10):1816-1820.
[187]Kyung-Soon L,Kageura K.Virtual relevant documents in text categorization with support vector machines[J].Information Processing & Management,2007.43(4):902-913.
[188]Lee K S,Kageura K.Virtual relevant documents in text categorization with support vector machines[J].Information Processing & Management,2007.43(4):902-913.
[189]Chen L,Huang J,Gong Z H.An anti-noise text categorization method based on support vector machines[J].Advances in Web Intelligence, Proceedings,2005.3528:272-278.
[190]Lin X D,Peng H,Liu B,et al.Support vector machines for text categorization in Chinese question classification[J] . 2006 IEEE/WIC/ACM International Conference on Web Intelligence, (WI 2006 Main Conference Proceedings),2006:334-337.
[191]Suykens J A K,Vandewalle J.Recurrent least squares support vector machines[J].Ieee Transactions on Circuits and Systems I-Fundamental Theory and Applications ,2000.47(7):1109-1114.
[192]Viaene S,Baesens B,Van Gestel T,et al.Knowledge discovery in a direct marketing case using least squares support vector machines[J] . International Journal of Intelligent Systems,2001.16(9):1023-1036.
[193]Van Gestel T,Suykens J A K,Lanckriet G,et al.Bayesian framework for least-squares support vector machine classifiers, Gaussian processes, and kernel Fisher discriminant analysis[J].Neural Computation,2002.14(5):1115-1147.
[194]Liu J H,Chan J P,Jiang S,et al.Online LS-SVM for function estimation and classification[J].Journal of University of Science and Technology Beijing,2003.10(5):73-77.
[195]Lu C,Van Gestel T,Suykens J A K,et al.Preoperative prediction of malignancy of ovarian tumors using least squares support vector machines[J].Artificial Intelligence in Medicine,2003.28(3):281-306.
[196]Tsujinishi D,Abe S.Fuzzy least squares support vector machines for multiclass problems[C]. in INNS/IEEE International Joint Conference on Neural Network (IJCNN 03).2003.Portland, Oregon.
[197]Cawley G C,Talbot N L C.Fast exact leave-one-out cross-validation of sparse least-squares support vector machines[J].Neural Networks,2004.17(10):1467-1475.
[198]van Gestel T,Suykens J A K,Baesens B,et al.Benchmarking least squares support vector machine classifiers[J].Mach Learn,2004.54(1):5-32.
[199]Acir N.Classification of ECG beats by using a fast least square support vector machineswith a dynamic programming feature selection algorithm[J] . Neural Computing & Applications,2005.14(4):299-309.
[200]Goethals I,Pelckmans K,Suykens J A K,et al.Subspace identification of Hammerstein systems using least squares support vector machines[J].Ieee Transactions on Automatic Control,2005.50(10):1509-1519.
[201]Borin A,Ferrao M F,Mello C,et al.Least-squares support vector machines and near infrared spectroscopy for quantification of common adulterants in powdered milk[J].Analytica Chimica Acta,2006.579(1):25-32.
[202]Tang E K,Suganthan P N,Yao X.Gene selection algorithms for microarray data based on least squares support vector machine[J].Bmc Bioinformatics,2006.7.
[203]Li J Z,Liu H X,Yao X J,et al.Structure-activity relationship study of oxindole-based inhibitors of cyclin-dependent kinases based on least-squares support vector machines[J].Analytica Chimica Acta,2007.581(2):333-342.
[204]Pelckmans K,Suykens J A K,Van Gestel T,et al.LS-SVMlab: A Matlab/C Toolbox for Least Squares Support Vector Machines[EB/OL].