宽带雷达目标极化特征提取与核方法识别研究
|
摘要
雷达目标特征提取与分类器设计是雷达目标识别系统中的两个关键问题。本文基于宽带高分辨全极化雷达体制,以飞机、坦克、舰船等军事目标为识别对象,围绕目标极化特征提取、优选、核方法分类器模型参数优化、核方法分类器设计这几个方面展开深入研究,以求提取反映目标本质属性的特征、设计目标识别最优分类器,提高雷达目标识别性能。研究内容主要分为三大部分:
1.雷达目标HRRP极化特征提取与优选
(1)从三个不同角度研究目标高分辨距离像(High Resolution Range Profile,HRRP)极化特征提取:①将极化合成孔径雷达(Synthetic Aperture Radar,SAR)中常用的H /α分解方法引入到全极化目标HRRP,丰富了反映目标HRRP散射随机性的特征;②根据Sinclair散射矩阵相似性参数理论,定义了目标HRRP与6种标准体散射矩阵相似性参数的概率形式,以其为基础构建的特征可准确反映目标的物理结构特性;③提出了反映目标散射能量特性的Mueller矩阵相似性参数特征,并且证明了该参数特征的旋转不变性。
(2)基于全极化与双极化体制,分别详细推导了这些特征在两种极化体制下的表达式。结果表明,两种体制下不仅这些特征的表达式不同,而且特征的个数也不同,有些特征之间存在一定的线性关系。实验部分采用飞机目标实测数据和舰船目标电磁特性软件计算数据从特征可分性以及识别性能两个方面验证以上特征的有效性。通过特征优选,分别为飞机、舰船目标提取有利于识别的极化特征。
2.雷达目标识别中SVM可分性研究与模型优化选择
(1)从理论上研究了支持矢量机(Support Vector Machine,SVM)线性可分性的本质,以及SVM线性不可分时引入惩罚因子c的物理意义,并且证明过程更简单,物理意义更明确。通过实验定量分析了核函数及其参数对识别性能的影响。
(2)从理论上解释了SVM模型最优选择的本质,分析了SVM模型单参数优化选择的方法,指出了单参数模型对于识别的局限性,提出进行模型多参数选择的必要性,并且提出了非均衡数据识别中SVM模型多参数最优选择方法。实验部分采用国际通用的标准数据库和雷达目标极化特征作为输入,验证了优选后多参数模型能够有效提高目标识别性能。
3.宽带雷达目标识别核方法分类器设计与核矩阵构造
(1)在目标可分性比较差的情况下,标准的支持矢量数据描述(Support Vector Data Description,SVDD)判决方法会使雷达目标识别出现拒分与错分问题。针对这个问题,提出了SVDD多目标模糊识别方法。多个极化状态下雷达目标HRRP识别实验结果表明,该方法简单实用,能够有效克服标准判决方法带来的错分与拒分问题。
(2)目标可分性比较差同样会使得SVM识别方法带来错分与拒分问题,针对这个问题提出了模糊SVM(FSVM)方法的判决改进策略,给出了简单实用的新判决策略。实验结果验证了该判决策略能够有效的提高目标识别性能。
(3)针对目标样本在高维特征空间中不能线性分类这一问题,提出了特征空间中数据核矩阵收缩新方法。主要思路是改变数据在特征空间的结构,使得线性不可分的数据变得线性可分,以此提高目标识别率。该项研究内容首先从理论上证明了收缩后核矩阵性能更优,然后推导了收缩后核矩阵的表示形式。采用飞机、坦克HRRP数据的识别实验验证了新方法的有效性。
Radar target feature extraction and classifier design are key problems in radar target recognition system. In order to extract the features reflecting target’s characteristic, construct the optimal classifier and improve radar target recognition performance, this dissertation regards the plane, tank and ship as the recognizing objects, focuses on polarimetric feature extraction, optimal selection, kernel methods classifiers model optimization and design based on wideband high-resolution fully polarimetric radar. The whole work contains three parts:
1. Radar Target HRRP Polarimetric Feature Extraction and Optimal Selection
(1) Polarimetric features extraction and optimal selection in radar target HRRP are mainly researched in three different aspects:①the entropy is introduced into fully polarimetric HRRP, which is usually used in polarimetric SAR, then the entropy feature based on HRRP is proposed, these parameters reflect the HRRP’s degree of scattering randomness;②according to similarity theory between two Sinclair scattering matrix, six probability description features are defined between HRRPs and some standard objects, the parameters reflect the target’s physics structure;③the similarity feature based on Mueller matrix is proposed, which reflects the target power characteristic. It is also proved that this feature does not vary with the orientation angle.
(2) The formulas of the three kinds of polarimetric feature are deduced in fully and dual polarimetric radar. The results revel that every feature formula is different in fully and dual polarimetric radar, some of them are correlative linearly. To verify the features’utility, some experiments had been done in feature separability and recognition performance by using some experimental data on planes and ships. The experimental results present that the features of HRRP proposed in dissertation are robust and well separability.
2. The Study on the Separability of SVM and Model Optimal Selection Used in Radar Target Recognition
(1) The sufficient necessary condition of linear separability is proved using a brief and clear method, and also proved and explained the new essential of penalty factor in SVM. The effects on the recognition performance of kernel function and its parameters are quantitatively analyzed.
(2) The essential of model optimal selection is explained and the method of model single parameter selection is analyzed. Some limitations of single parameter model are presented, and then the connotation and necessity of SVM model multi-parameter selection are theoretically analyzed. SVM model optimal multi-parameter selection method for imbalanced data recognition is proposed. Some experiments on Benchmarks and radar target polarimetric features verify that the new method can improve the recognition performance effectively.
3. Kernel Method Classifier Design for Radar Target Recognition and Kernel Matrix Construction
(1) Some misclassification and reject classification problems caused by the standard SVDD decision strategy always occur especially in lack of separability of radar target, so radar target fuzzy recognition method is proposed based on SVDD. Experimental results on the fully polarimetric HRRP data present that the fuzzy recognition method can achieve better accuracy than standard SVDD.
(2) Lack of separability of radar target can also cause misclassification and reject classification problems using SVM. To deal with these problems, the improved decision strategy is proposed based on FSVM. Simulations verify the new method.
(3) To deal with the linear non-separability, the new conception and method of kernel matrix contraction are proposed. The main idea of the new method is to make the non-separable data separability by changing the construction of data in feature space. The kernel matrix of contracted pattern is deduced, the separability of contracted pattern is also proved more excellent than before. The experiments conducted to evaluate the performance of the new method are mainly in two aspects: the measurement and classifier performance of kernel matrix. The results on planes and ships HRRP data reveal that the performance of contracted kernel matrix is more excellent than before.
1.雷达目标HRRP极化特征提取与优选
(1)从三个不同角度研究目标高分辨距离像(High Resolution Range Profile,HRRP)极化特征提取:①将极化合成孔径雷达(Synthetic Aperture Radar,SAR)中常用的H /α分解方法引入到全极化目标HRRP,丰富了反映目标HRRP散射随机性的特征;②根据Sinclair散射矩阵相似性参数理论,定义了目标HRRP与6种标准体散射矩阵相似性参数的概率形式,以其为基础构建的特征可准确反映目标的物理结构特性;③提出了反映目标散射能量特性的Mueller矩阵相似性参数特征,并且证明了该参数特征的旋转不变性。
(2)基于全极化与双极化体制,分别详细推导了这些特征在两种极化体制下的表达式。结果表明,两种体制下不仅这些特征的表达式不同,而且特征的个数也不同,有些特征之间存在一定的线性关系。实验部分采用飞机目标实测数据和舰船目标电磁特性软件计算数据从特征可分性以及识别性能两个方面验证以上特征的有效性。通过特征优选,分别为飞机、舰船目标提取有利于识别的极化特征。
2.雷达目标识别中SVM可分性研究与模型优化选择
(1)从理论上研究了支持矢量机(Support Vector Machine,SVM)线性可分性的本质,以及SVM线性不可分时引入惩罚因子c的物理意义,并且证明过程更简单,物理意义更明确。通过实验定量分析了核函数及其参数对识别性能的影响。
(2)从理论上解释了SVM模型最优选择的本质,分析了SVM模型单参数优化选择的方法,指出了单参数模型对于识别的局限性,提出进行模型多参数选择的必要性,并且提出了非均衡数据识别中SVM模型多参数最优选择方法。实验部分采用国际通用的标准数据库和雷达目标极化特征作为输入,验证了优选后多参数模型能够有效提高目标识别性能。
3.宽带雷达目标识别核方法分类器设计与核矩阵构造
(1)在目标可分性比较差的情况下,标准的支持矢量数据描述(Support Vector Data Description,SVDD)判决方法会使雷达目标识别出现拒分与错分问题。针对这个问题,提出了SVDD多目标模糊识别方法。多个极化状态下雷达目标HRRP识别实验结果表明,该方法简单实用,能够有效克服标准判决方法带来的错分与拒分问题。
(2)目标可分性比较差同样会使得SVM识别方法带来错分与拒分问题,针对这个问题提出了模糊SVM(FSVM)方法的判决改进策略,给出了简单实用的新判决策略。实验结果验证了该判决策略能够有效的提高目标识别性能。
(3)针对目标样本在高维特征空间中不能线性分类这一问题,提出了特征空间中数据核矩阵收缩新方法。主要思路是改变数据在特征空间的结构,使得线性不可分的数据变得线性可分,以此提高目标识别率。该项研究内容首先从理论上证明了收缩后核矩阵性能更优,然后推导了收缩后核矩阵的表示形式。采用飞机、坦克HRRP数据的识别实验验证了新方法的有效性。
Radar target feature extraction and classifier design are key problems in radar target recognition system. In order to extract the features reflecting target’s characteristic, construct the optimal classifier and improve radar target recognition performance, this dissertation regards the plane, tank and ship as the recognizing objects, focuses on polarimetric feature extraction, optimal selection, kernel methods classifiers model optimization and design based on wideband high-resolution fully polarimetric radar. The whole work contains three parts:
1. Radar Target HRRP Polarimetric Feature Extraction and Optimal Selection
(1) Polarimetric features extraction and optimal selection in radar target HRRP are mainly researched in three different aspects:①the entropy is introduced into fully polarimetric HRRP, which is usually used in polarimetric SAR, then the entropy feature based on HRRP is proposed, these parameters reflect the HRRP’s degree of scattering randomness;②according to similarity theory between two Sinclair scattering matrix, six probability description features are defined between HRRPs and some standard objects, the parameters reflect the target’s physics structure;③the similarity feature based on Mueller matrix is proposed, which reflects the target power characteristic. It is also proved that this feature does not vary with the orientation angle.
(2) The formulas of the three kinds of polarimetric feature are deduced in fully and dual polarimetric radar. The results revel that every feature formula is different in fully and dual polarimetric radar, some of them are correlative linearly. To verify the features’utility, some experiments had been done in feature separability and recognition performance by using some experimental data on planes and ships. The experimental results present that the features of HRRP proposed in dissertation are robust and well separability.
2. The Study on the Separability of SVM and Model Optimal Selection Used in Radar Target Recognition
(1) The sufficient necessary condition of linear separability is proved using a brief and clear method, and also proved and explained the new essential of penalty factor in SVM. The effects on the recognition performance of kernel function and its parameters are quantitatively analyzed.
(2) The essential of model optimal selection is explained and the method of model single parameter selection is analyzed. Some limitations of single parameter model are presented, and then the connotation and necessity of SVM model multi-parameter selection are theoretically analyzed. SVM model optimal multi-parameter selection method for imbalanced data recognition is proposed. Some experiments on Benchmarks and radar target polarimetric features verify that the new method can improve the recognition performance effectively.
3. Kernel Method Classifier Design for Radar Target Recognition and Kernel Matrix Construction
(1) Some misclassification and reject classification problems caused by the standard SVDD decision strategy always occur especially in lack of separability of radar target, so radar target fuzzy recognition method is proposed based on SVDD. Experimental results on the fully polarimetric HRRP data present that the fuzzy recognition method can achieve better accuracy than standard SVDD.
(2) Lack of separability of radar target can also cause misclassification and reject classification problems using SVM. To deal with these problems, the improved decision strategy is proposed based on FSVM. Simulations verify the new method.
(3) To deal with the linear non-separability, the new conception and method of kernel matrix contraction are proposed. The main idea of the new method is to make the non-separable data separability by changing the construction of data in feature space. The kernel matrix of contracted pattern is deduced, the separability of contracted pattern is also proved more excellent than before. The experiments conducted to evaluate the performance of the new method are mainly in two aspects: the measurement and classifier performance of kernel matrix. The results on planes and ships HRRP data reveal that the performance of contracted kernel matrix is more excellent than before.
引文
[1]黄培康,殷红成,许小剑.雷达目标特性[M].北京:电子工业出版社, 2005.
[2] Huynen J R. Phenomenological theory of radar targets[D]. Delft, The Netherlands: Technical University of Delft, 1970.
[3] van Zyl J J. Unsupervised classification of scattering behavior using radar polarimetry data[J]. IEEE Transactions on Geoscience and Remote Sensing, 1989, 27(1): 36-45.
[4] Krogager E. New decomposition of the radar target scattering matrix[J]. Electronics Letters, 1990, 26(18): 1525-1527.
[5] Pottier E, Cloude S R. Application of the H/A/αpolarimetric decomposition theorems for land classification[C]. Proceedings of SPIE Conference on Wideband Interferometric Sensing and Imaging Polarimetry, San Diego, CA, USA, 1997: 132-143.
[6] Cloude S R, Pottier E. An entropy based classification scheme for land applications of polarimetric SAR[J]. IEEE Transactions on Geoscience and Remote Sensing, 1997, 35(1): 549-557.
[7] Lee J S, Grunes M R, Ainsworth T L, et al. Unsupervised classification using polarimetric decomposition and the complex Wishart classifier[J]. IEEE Transactions on Geoscience and Remote Sensing, 1999, 37(5): 2249-2258.
[8] Yang J, Peng Y N, Lin S M. Similarity between two scattering matrices[J]. Electronic Letters, 2001, 37(3): 193-194.
[9]徐俊毅,杨健,彭应宁.双波段极化雷达遥感图像分类的新方法[J].中国科学E辑, 2005, 35(10): 1083-1095.
[10] Berizzi F, Martorella M, Capria A. H/αpolarimetric features for man-made target classification[C]. IEEE Radar Conference, Rome, Italy, 2008: 1596-1601.
[11] Kostinski A B, Boerner W M. On foundations of radar polarimetry[J]. IEEE Transactions on Antennas and Propagation, 1986, 34(12): 1395-1404
[12] Vapnik V N. The nature of statistical learning theory[M]. New York: Springer, 2000.
[13]王雪松.宽带极化信息处理的研究[D].长沙:国防科技大学博士学位论文, 1999.
[14]吴永辉.极化SAR图像分类技术研究[D].长沙:国防科技大学博士学位论文, 2007.
[15]刘秀清.全极化合成孔径雷达极化信息处理技术研究[D].北京:中国科学院电子学研究所博士学位论文, 2004.
[16]周晓光.极化SAR图像分类方法研究[D].长沙:国防科技大学博士学位论文, 2008.
[17] Li H J, Wang Y D, Wang L H. Matching score properties between range profiles of high-resolution radar targets[J]. IEEE Transactions Antennas and Propagation, 1996, 44(4): 444-452.
[18] Cohen M N. Variability of ultra-high range resolution radar profiles and some implications for target recognition[C]. Proceedings of SPIE Conference on Signal Processing, Sensor Fusion and Target Recognition, 1992, 1699: 256-266.
[19]闫锦.基于高距离分辨像的雷达目标识别研究[D].北京:中国航天第二研究院博士学位论文, 2004.
[20] Cameron W L, Leung L K. Feature motivated polarization scattering matrix decomposition[C]. Proceedings of IEEE International Radar Conference, Arlington, VA, May 7-10, 1990: 549-557.
[21]李鸣.毫米波频率步进雷达前端关键技术研究[D].南京:南京理工大学博士学位论文, 2007.
[22]龙腾,毛二可,何佩琨.调频步进雷达信号分析与处理[J].电子学报, 1998, 26(12): 84-88.
[23]孙即祥.现代模式识别[M].长沙:国防科技大学出版社, 2002.
[24]杜兰.雷达高分辨距离像目标识别方法研究[D].西安:西安电子科技大学博士学位论文, 2007.
[25]胡磊.基于粗糙集理论的雷达目标高分辨距离像识别[D].长沙:国防科技大学硕士学位论文, 2008.
[26] Theodoridis S, Koutroumbas K著,李晶皎,朱志良,王爱侠等译.模式识别(第2版)[M].北京:电子工业出版社, 2004.
[27]熊洪允,曾绍标,毛云英著.应用数学基础[M].天津:天津大学出版社, 1998.
[28] Elizondo D. The linear separability problem: some testing methods[J]. IEEE Transactions on Neural networks, 2006, 17(2): 330-344.
[29] Ben-Israel A, Levin Y. The geometry of linear separability in data sets[J]. Linear Algebra and its Applications 2006, 416(1): 75-87.
[30] Pranckevicience E, Ho T K, Somorjai R. Class separability in space reduced by feature selection[C]. The 18th International Conference on Pattern Recognition (ICPR’06), Hong Kong, 2006, Vol.3: 254-257.
[31]吴涛.核函数的性质、方法及其在障碍检测中的应用[D].长沙:国防科技大学博士学位论文, 2003.
[32] Tax D M J, Duin R P W. Support vector data description [J]. Machine Learning, 2004, 54(1): 45-66.国防科学技术大学研究生院博士学位论文
[33]吴翊,屈田兴.应用泛函分析[M].长沙:国防科技大学出版社, 2002: 52-59.
[34]张铃.支持向量机理论与基于规划的神经网络学习算法[J].计算机学报, 2001, 24(2): 113-118.
[35] Cortes C, Vapnik V N. Support vector networks [J]. Machine Learning, 1995, 20(3): 273-297.
[36] Shawe-Taylor J, Cristianini N. Kernel methods for pattern analysis [M]. UK: Cambridge University Press, 2004.
[37]刘向东,骆斌,陈兆乾.支持向量机最优模型选择的研究[J].计算机研究与发展, 2005, 42(4): 576-581.
[38] R?tsch G. Benchmarks data sets. http://ida.first.fraunhofer.de/projects/bench /benchmarks.htm. 2003.7
[39] R?tsch G, Ondda T, Muller K R. Soft margins for AdaBoost[J]. Machine Learning, 2001, 42(3): 287-320.
[40] Hastie T, Rosset S, Tibshirani R, et al. The entire regularization path for the support vector machine [J]. Journal of Machine Learning Research, 2004, 5(12): 1391-1415.
[41] Lee M S, Keerthi S S, Ong C J, et al. An efficient method for computing leave-one-out error in support vector machines with Gaussian kernels[J]. IEEE Transactions on Neural Networks, 2004, 15(3): 750-757.
[42] Xu P, Chan A K. An efficient algorithm on multi-class support vector machine model selection[C]. Proceedings of the International Joint Conference on Neural Networks 2003. Portland, IEEE, 2003: 3229-3232.
[43] Chapelle O, Vapnik V N, Bousquet O, et al. Choosing multiple parameters for support vector machines[J]. Machine Learning, 2002, 16(1): 131-159.
[44] Keerthi S S. Efficient tuning of SVM hyper parameters using radius/margin bound and iterative algorithms[J]. IEEE Transactions on Neural Networks, 2002, 13(5): 1225-1229.
[45] Musicant D R, Kumar V, Ozgur A. Optimizing F-measure with support vector machines[C]. In the Sixteenth International Florida Artificial Intelligence Research Society Conference, St. Augustine, Florida, USA, 2003: 356-360.
[46] Agarwal R, Joshi M V. PNrule: a new framework for learning classifier models in data mining (a case-study in network intrusion detection)[C]. Proceedings of First SIAM International Conference on Data Mining, 2001: 1-30.
[47]李敏强,寇纪淞,林丹.遗传算法的基本理论与应用[M].北京:科学出版社,2002.
[48]刘守生.遗传算法与小波神经网络中若干问题的研究[D].南京:南京航空航天大学博士学位论文, 2004.
[49]李良敏,温广瑞,王生昌.基于遗传算法的改进径向基支持矢量机及其应用[J].系统仿真学报, 2008, 20(22): 6088-6092.
[50] Cortes C, Vapnik V N. Support vector networks[J]. Machine Learning, 1995, 20(3): 273-297.
[51] Morik K, Brockhausen P, Joachims T. Combining statistical learning with a knowledge-based approach- a case study in intensive care monitoring[C]. Proceedings of the 16th International Conference on Machine Learning. San Mateo, Canada: Morgan Kaufman Publishers, 1999: 268-277.
[52] Takuya I, Shigeo A. Fuzzy support vector machine for pattern classification[C]. Proceeding of International Joint Conference on Neural Networks, Washington, D.C, 2001: 1449-1454.
[53] Bo L F, Wang L, Jiao L C. Multiple parameter selection for LS-SVM using smooth leave-one-out error[J]. Lecture Notes in Computer Science, Berlin: Springer, 2005: 851-856.
[54] Peng X Y, Wu H X, Peng Y. Parameter selection method for SVM with PSO[J]. Chinese Journal of Electronics, 2006, 15(4): 638-642.
[55] Bi L P, Huang H, Zheng Z Y. New heuristic for determination Gaussian kernel parameter[C]. Proceedings of the Fourth International Conference on Machine Learning and Cybernetics, Guangzhou, 2005: 4299-4304.
[56] Eitrich T, Lang B. Efficient Optimization of support vector machine learning parameters for unbalanced datasets[J]. Journal of computational and applied mathematics, 2006, 196(2): 425-436.
[57] Lee M S, Keerthi S S. An efficient method for computing Leave-One-Out error in Support Vector Machines with Gaussian kernels[J]. IEEE Transactions on Neural Networks, 2004, 15(3): 750-757.
[58] Jaakkola T S, Haussler D. Probabilistic kernel regression models[C]. Proceedings of the 1999 Conference on AI and Statistics, 1999.
[59] Opper M, Winther O. Gaussian process and SVM: Mean field and Leave-One- Out[J]. Advances in Large Margin Classifiers, MIT Press, 2000: 311-326.
[60] Chung K M, Sun C L. Radius margin bounds for Support Vector Machines with RBF kernel[C]. Proceedings of the 9th International Conference on Neural Information, 2002, 2: 893-897.
[61] Chung K M, Kao W C, Sun C L. Radius margin bounds for Support Vector Machines with RBF kernel[J]. Neural Computing, 2003, 15(11): 2643-2681.
[62] Vapnik V N, Chapelle O. Bounds on error expectation for SVM[J]. Advances in Large Margin Classifiers, MIT Press, 2000: 261-280.
[63]郑松峰.支撑向量机的模型和参数选择研究[D].西安:西安交通大学硕士学位论文, 2003.
[64]沈明华.散射中心分布特征提取与核方法分类器关键技术研究[D].长沙:国防科技大学博士学位论文, 2007.
[65] Lanckriet G R G, Cristianini N, Bartlett P, Ghaoui L E, et al. Learning the kernel matrix with semi-definite programming[J]. Journal of Machine Learning Research, 2004, 5(12): 27-72.
[66] Ayatt N E, Cheriet M, Suen C Y. Automatic model selection for the optimization of SVM kernels[J]. Pattern Recognition, 2005, 38(10): 1733-1745.
[67] Friedrichs F, Igel C. Evolutionary tuning of multiple SVM parameters[J]. Neural Computing, 2005, 64(3): 107-117.
[68] Schittkowski K. Optimal parameter selection in support vector machines[J]. Journal of Industrial and Management Optimization, 2005, 1(4): 465-476.
[69] Gold C, Holub A, Sollich P. Bayesian approach to feature selection and parameter tuning for support vector machine classifiers[J]. Neural Networks, 2005, 18(5-6): 693-701.
[70] Rennie J D M. Derivation of the F-measure[EB/OL]. http://people.csail.mit.edu/ ~jrennie/writing, 2004.
[71] Abouzakhar N S, Manson G A. Evaluation of intelligent intrusion detection models[J]. International Journal of Digital Evidence, 2004, 3(1): 1-20.
[72] Sch?lkopf B, Smola A, Müller K R. Nonlinear component analysis as a kernel eigenvalue problem[J]. Neural Computation, 1998, 10(5): 1299-1319.
[73] Xu Y, Zhang D, Song FX, et al. A method for speeding up feature extraction based on KPCA[J]. Neural Computing, 2007, 70(4-6): 1056-1061.
[74] Liu Y H, Chen Y T, Lu S S. Face detection using kernel PCA and imbalanced SVM[J]. Lecture Notes in Computer Science, Berlin: Springer-Verlag, 2006, 4221: 351-360.
[75] Li W H, Gong WG, Liang Y X, et al. Feature selection based on KPCA, SVM and GSFS for face recognition[J]. Lecture Notes in Computer Science, Berlin: Springer-Verlag, 2005, 3687: 344-350.
[76] Gu Y F, Zhang Y. Hyperspectral small target detection by combining kernel PCA with linear mixture model[J]. Chinese Journal of Electronics, 2005, 14(1): 130-134.
[77] Mika S, R?tsch G, Weston J, et al. Fisher discriminant analysis with kernels[J]. Neural Networks for Signal Processing IX. New York: IEEE Press, 1999: 41-48.
[78] Zhang J F, Huang Z C. Novelty detection methods and novel fault class detection[C]. 1ST International Symposium on Digital Manufacture, 2006, 1-3: 767-771.
[79] Lee S W, Park J Y, Lee S W. Low resolution face recognition based on support vector data description[J]. Pattern Recognition. 2006, 39(9): 1809-1812.
[80] Park J, Kang D, Kwok J T, et al. Facial image reconstruction by SVDD-basedpattern de-noising[J]. Lecture Notes in Computer Science, Berlin: Springer- Verlag, 2006, 3832: 129-135.
[81] Pan M Q, Qian S X, Lei L Y, et al. Support vector data description with model selection for condition monitoring[C]. Proceedings of 2005 International Conference on Machine Learning and Cybernetics, 2005, 1: 4315-4318.
[82]郭桂蓉,庄钊文.信息处理中的模糊技术[M].长沙:国防科技大学出版社, 1993.
[83]肖怀铁.宽带极化毫米波雷达目标特征信号测量与识别算法研究[D].长沙:国防科技大学博士学位论文, 2000.
[84] Blake C L, Merz C J. UCI repository of learning database[DB/OL], Available at http://www.ics.uci.edu/~mlearn/MLRepository.html, 1998.
[85] Takuya I, Shigeo A. Fuzzy Support Vector Machine for pattern classification[C]. Proceedings of International Joint Conference on Neural Networks, Washington, D.C, 2001, 7: 1449-1454.
[86] Daisuke T, Shigeo A. Fuzzy Least Squares Support Vector Machines for multiclass problems[J]. Neural Networks, 2003, 16(5-6): 785-792.
[87] Lin C F, Wang S D. Fuzzy support vector machines[J]. IEEE Transactions on Neural Networks, 2002, 13(2): 464-471.
[88] Cristianini N, Shawe-Taylor J著,李国正,王猛,曾华军译.支持向量机导论[M].北京:电子工业出版社, 2006.
[89] Peng X Y, Wu H X, Peng Y. Parameter selection method for SVM with PSO[J]. Chinese Journal of Electronics, 2006, 15(4): 638-642.
[90] Bennett K, Champbell O. Support vector machines: hyper or hallelujah?[J]. SIGKDD Explorations, 2000, 2(2): 1-13.
[91]陶卿,孙德敏,范劲松.基于闭凸包收缩的最大边缘线性分类器[J].软件学报, 2002, 13(3): 404-409.
[92] Kang W S, Ki H I, Jin Y C. SVDD-based method for fast training of multi-class Support Vector Classifier[J]. Lecture Notes on Computer Science, Berlin: Springer-Verlag, 2006, 3971: 991-996.
[93]孙文峰.雷达目标识别技术述评[J].雷达与对抗, 2001(3): 1-8.
[94]庄钊文,肖顺平,王雪松.雷达极化信息处理及应用[M].北京:国防工业出版社,1999.
[95]代大海.极化雷达成像及目标特征提取研究[D].长沙:国防科技大学博士学位论文, 2008.
[96]王涛.弹道中段目标极化域特征提取与识别[D].长沙:国防科技大学博士学位论文, 2006.
[97]李永祯,王雪松,徐振海等.基于强散射点径向积累的高分辨极化目标检测研究[J].电子学报, 2001, 29(3): 307-310.
[98]曾勇虎.极化雷达时频分析与目标识别的研究[D].长沙:国防科技大学博士学位论文, 2004.
[99]代大海,王雪松,肖顺平.基于二维CP-GTD模型的全极化ISAR超分辨成像[J].自然科学进展. 2007, 17(9): 131-140.
[100] Steadly W M, Moses R L. High resolution exponential modeling of fully polarized radar returns[J]. IEEE Transactions on Aerospace and Electronic Systems, 1991, 27(3): 459-468.
[101]庄钊文,黎湘,刘永祥.智能化武器系统发展的关键技术—雷达自动目标识别技术[J].科技导报, 2005, 23(8): 20-23.
[102] Chamberlain N F, Walton E K Garber F D. Radar target identification of aircraft using polarization diverse features[J]. IEEE Transactions on Aerospace and Electronic Systems, 1991, 27(1): 58-67.
[103]陈曾平.雷达目标结构特征识别的理论与应用[D].长沙:国防科技大学博士学位论文, 1994.
[104]郭桂蓉,庄钊文,陈曾平.电磁特征抽取与目标识别[M].长沙:国防科技大学出版社, 1995.
[105]何松华,郭桂蓉,庄钊文.雷达目标高分辨率距离-极化结构成像方法研究[J].电子学报, 1994, 22(7): 1-7.
[106]何松华.高距离分辨力毫米波雷达目标识别的理论与应用[D].长沙:国防科技大学博士学位论文, 1993.
[107]肖顺平,郭桂蓉,庄钊文,王雪松.基于极化谱的飞机目标识别[J].电子学报, 1997, 25(12): 60-64.
[108]肖顺平,王雪松,庄钊文.基于极化不变量的飞机目标识别[J].红外与毫米波学报,1996, 15(6): 439-444.
[109]肖顺平,郭桂蓉,王雪松.基于极化频率稳定度的目标识别[J].现代雷达, 1995, 17(5): 1-7.
[110]李盾,肖顺平,王雪松.基于回波趋向伪本征极化特性的目标识别研究[J].电子学报, 1999, 27(9): 1-4.
[111] Ferrazzoli P, Paloscia S, Pampaloni P, et al. The potential of multifrequency polarimetric SAR in assessing agricultural and arboreous biomass[J]. IEEE Transactions on Geoscience and Remote Sensing, 1997, 35(1): 5-17.
[112] Ferrazzoli P, Guerriero L, Schiavon G. Experimental and model investigation on radar classification capability[J]. IEEE Transactions on Geoscience and Remote Sensing, 1999, 37(2): 960-968.
[113] Pellizzeri T M, Lombardo P. Model-based processing of multifrequencypolarimetric SAR images of urban areas[C]. Proceedings of GRSS/ISPRS Joint Workshop on Data Fusion and Remote Sensing over Urban Areas, 2003: 47-51.
[114] Lombardo P. Optimal classification of polarimetric SAR images using segmentation[C]. Proceedings of IEEE International Radar Conference, Long Beach, California, 2002: 8-13.
[115] Lee J S, Krogager E, Ainsworth T L, et al. Polarimetric analysis of radar signature of a manmade structure[J]. IEEE Transactions on Geoscience and Remote Sensing letters, 2006, 3(4): 555-559.
[116] Karnychev V, Valery A K, Leo P L, et al. Algorithms for estimating the complete group of polarization invariants of the scattering matrix (SM) based on measuring all SM elements[J]. IEEE Transactions on Geoscience and Remote Sensing, 2004, 42(3): 529-539.
[117]张澄波.综合孔径雷达原理、系统分析与应用[M].北京:科学出版社, 1989.
[118]李盾,肖顺平,王雪松等.基于极化度分布特征的目标识别方法研究[J].电子科学学刊, 2000, 22(6): 994-1000.
[119] Dong Y H, Forster B C, Ticehurst C. A new decomposition of radar polarization signatures[J]. IEEE Transactions on Geoscience and Remote Sensing, 1998, 36(3): 933-939.
[120] Seisuke F, Haruto H. A wavelet-based texture feature set applied to classification of multifrequency polarimetric SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 1999, 37(5): 2282-2286.
[121] Khan K U, Yang J. Novel features for polarimetric SAR image classification by neural network[C]. Proceedings of International Conference on Neural Networks and Brain, 2005: 165-170.
[122] Cloude S R, Pottier E. A review of target decomposition theorems in radar polarimetry[J]. IEEE Transactions on Geoscience and Remote Sensing, 1996, 34(2): 498-518.
[123] Touzi R, Raney R K, Charbonneau F. On the use of permanent symmetric scatterers for ship characterization[J]. IEEE Transactions on Geoscience and Remote Sensing, 2004, 42(10): 2039-2045.
[124] van Zyl J J. Unsupervised classification of scattering behavior using radar polarimetry data[J]. IEEE Transactions on Geoscience and Remote Sensing, 1989, 27(1): 36–45.
[125] Freeman A, Durden S. Three-component scattering model to describe polarimetric SAR data[C]. Proceedings of SPIE Radar Polarimetry Conference, San Diego, CA, 1992: 213–224.
[126] Dong Y H, Forster B C, Ticehurst C. A new decomposition of radar polarization signatures[J]. IEEE Transactions on Geoscience and Remote Sensing, 1998,36(3):933-939.
[127] Krogager E. Decomposition of the radar target scattering matrix with application to high resolution target imaging[C]. Proceedings of IEEE National Telesystems Conference (NTC’91), Atlanta, GA, USA, 1991: 77-82.
[128] Krogager E. Wideband Pol-SAR/ISAR signal and image processing for target identification and discrimination[C]. Proceedings of IEEE Asia-Pacific Microwave Conference, Adelaide, Melbourne, Australia, 1992: 947-950.
[129]李莹,任勇,山秀明.基于目标分解的极化雷达飞机识别法[J].清华大学学报(自然科学版), 2001, 41(7): 32-35.
[130] Cameron W L, Youssef N N, Leung L K. Simulated polarimetric signatures of primitive geometrical shapes[J]. IEEE Transactions on Geoscience and Remote Sensing, 1996, 34(3): 793-803.
[131] Touzi R, Charbonneau F. Characterization of Target Symmetric Scattering Using Polarimetric SARs[J]. IEEE Transactions on Geoscience and Remote Sensing, 2002, 40(11): 2507-2516.
[132] Touzi R, Raney R K, Charbonneau F. On the use of permanent symmetric scatterers for ship characterization[J]. IEEE Transactions on Geoscience and Remote Sensing, 2004, 42(10): 2039-2045.
[133] Freeman A, Durden S L. A three-component scattering model for polarimetric SAR data[J]. IEEE Transactions on Geoscience and Remote Sensing, 1998, 36(3): 963-973.
[134] Yamaguchi Y, Moriyama T, Ishido M, et al. Four-component scattering model for polarimetric SAR image decomposition[J]. IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(8): 1699-1706.
[135] Yamaguchi Y, Yajima Y, Yamada H. A four-component decomposition of POLSAR images based on the coherency matrix[J]. IEEE Transactions on Geoscience and Remote Sensing Letters, 2006, 3(3): 292-296.
[136] Holm W A, Barnes R M. On radar polarization mixed target state decomposition techniques[C]. Proceedings of IEEE National Radar Conference, April 1988: 249-254.
[137] Ferro-Famil L, Pottier E, Lee J S. Unsupervised classification of multifrequency and fully polarimetric SAR images based on the H/A/Alpha-Wishart classifier[J]. IEEE Transactions on Geoscience and Remote Sensing, 2001, 39(11): 2332-2342.
[138] Lopez-Sanchez J M, Fortuny-Guasch J, Sieber A J. Experimental validation of an entropy-based classification scheme using a wide-band polarimetric radar[C]. Proceedings of International Geoscience and Remote Sensing Symposium, Seattle, Washington, USA, July 1998: 2381-2383.
[139] Park S E, Moon W M. Classification of the polarimetric SAR using fuzzy boundaries in entropy and alpha plane[C]. Proceedings of International Geoscience and Remote Sensing Symposium, Seoul, Korea, July 2005: 5517-5519.
[140] Lombardo P, Pellizzeri T M, Tomasuolo A. A joint classification technique based on multivariate annealing segmentation and "H/A/α" decomposition for fully polarimetric SAR images of suburban areas[C]. Proceedings of International Geoscience and Remote Sensing Symposium, Sydney, Australia, July 2001: 2922-2924.
[141] Praks J, Hallikainen M. A novel approach in polarimetric covariance matrix eigen-decomposition[C]. Proceedings of International Geoscience and Remote Sensing Symposium, Honolulu, Hawaii, USA, July 2000: 1119-1121.
[142]高明星,杨健,彭应宁.极化雷达遥感中目标特征提取[J],电波科学学报, 2004, 19(4): 418-421.
[143]王文光,王俊,毛士艺等.基于极化SAR图像分类的海上舰船目标检测[J].信号处理, 2007, 23(5):676-679.
[144]范立生,高明星,杨健等.极化SAR遥感中森林特征的提取[J].电波科学学报, 2005, 20(5): 553-556.
[145]董贵威,杨健,彭应宁等.极化SAR遥感中森林特征探测[J].清华大学学报(自然科学版), 2003, 43(7): 953-956.
[146]袁莉.基于高分辨距离像的雷达目标识别方法研究[D].西安:西安电子科技大学博士学位论文, 2007.
[147]廖学军.基于高分辨距离像的雷达目标识别[D].西安:西安电子科技大学博士学位论文, 1999.
[148]杜兰,保铮,邢孟道.飞机目标的雷达一维距离像特性研究[J].西安电子科技大学学报, 2002, 28(sup.): 14-19.
[149]杜兰,刘宏伟,保铮.利用目标方位信息改善雷达距离像识别性能[J].系统工程与电子技术. 2004, 26(8): 1040-1043.
[150]裴炳南.高分辨雷达自动目标识别方法研究[D].西安:西安电子科技大学博士学位论文, 2002.
[151]袁莉,刘宏伟,保铮.基于中心矩特征的雷达HRRP自动目标识别[J].电子学报, 2004, 32(12): 2078-2081.
[152]杜兰,刘宏伟,保铮.一种利用雷达目标高分辨距离像幅度起伏特性的特征提取新方法[J].电子学报, 2005, 33(3): 411-415.
[153] Cristianini N, Shawe-Taylor J. An introduction to support vector machines and other kernel-based learning methods[M]. UK, Cambridge: Cambridge UniversityPress, 2000.
[154]高学,金连文.一种基于支持向量机的手写汉字识别方法[J],电子学报, 2002, 30(5): 651-654.
[155]周志明,王以志,黄文芝等.基于小波和支持向量机的人脸识别技术[J].计算机工程与应用, 2004, 40(12): 52-54.
[156]朱永生.支持向量机及其在机械故障模式识别中的应用[D].西安:西安交通大学博士学位论文, 2003.
[157] Bruzzone L, Melgani F. Classification of hyperspectral images with support vector machines: multiclass strategies[C]. Proceedings of SPIE Image and Signal Processing for Remote Sensing, Bellingham, WA, 2004, 5238: 408-419.
[158] Serpico S B, Bruzzone L. A new search algorithm for feature selection in hyperspectral remote sensing images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2001, 39(7): 1360-1367.
[159] Brown M, Lewis H G, Gunn S R. Linear spectral mixture models and support vector machines for remote sensing[J]. IEEE Transactions on Geoscience and Remote Sensing, 2000, 38(5): 2346 -2360.
[160] Waagen D, Cassabaum M, Schmitt H, et al. Support vector machine optimization via margin distribution analysis[C]. Proceedings of SPIE Automatic Target Recognition, 2003, 5094: 348-357.
[161] Ma J S, Li H L, Ahalt S C. Using support vector machines as HRR signature classifiers[C]. Proceedings of SPIE Automatic Target Recognition, 2004, 4379: 209-215.
[162] Samanta B. Gear fault detection using artificial neural networks and support vectormachines with genetic algorithms[J]. Mechanical Systems and Signal Processing, 2004, 18: 625-644.
[163]张莉.支撑矢量机与核方法研究[D].西安:西安电子科技大学博士学位论文, 2002.
[164]周伟达.核机器学习方法研究[D].西安:西安电子科技大学博士学位论文, 2003.
[165] Peng X Y, Wu H X, Peng Y. Parameter selection method for SVM with PSO[J]. Chinese Journal of Electronics, 2006, 15(4): 638-642.
[166] Cover T M. Geometrical and statistical properties of systems of linear inequalities with applications in pattern recognition[J]. IEEE Transactions on Electronic Computers, 1965, 14(4): 326-334.
[167] Haykin S著,叶世伟,史忠植译.神经网络原理[M].北京:机械工业出版社, 2004.
[168] Tao X M, Liu F R, Zhou T X. A novel approach to intrusion detection based onsupport vector data description[C]. The 30th Annual Conference of the IEEE Industrial Electronics Society, Busan, Korea, November, 2004: 2016-2021.
[169] Banerjee A, Burlina P, Diehl C. A support vector method for anomaly detection in hyperspectral imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2006, 44(8): 2282-2291.
[170] Lee K Y, Kim D W, Lee K H, et al. Density-induced support vector data description[J]. IEEE Transactions on Neural Networks Letters, 2007, 18(1): 284-289.
[171] Sanchez-Hernandez C, Boyd D S, Foody G M. One class classification for mapping a specific land-cover class: SVDD classification of Fenland[J]. IEEE Transactions on Geoscience and Remote Sensing, 2007, 45(4): 1061-1072.
[172]陆从德,张太镒,胡金燕.基于乘性规则的支持向量域分类器[J].计算机学报, 2004, 27(5): 690-694.
[173]吴涛,贺汉根,贺明科.基于插值的核函数构造[J].计算机学报, 2003, 26(8): 990-996.
[174] Amari S, Wu S. Improving support vector machine classifiers by modifying kernel functions[J]. Neural Networks, 1999, 12(6): 783–789.
[175] Xiong H L, Swamy M N S, Omair-Ahmad M. Optimizing the kernel in the empirical feature space[J]. IEEE Transactions on Neural Networks, 2005, 16(2): 460-474.
[176] Micchelli C A,Pontil M. Learning the kernel function via regularization[J]. Journal of Machine Learning Research, 2005, 6(7): 1099-1125.
[177] Zhang M, Zhou G D, Aiti A. Exploring syntactic structured features over parse trees for relation extraction using kernel methods[J]. Information Processing and Management, 2007, 43(4): 969-982.
[178] Falcao A O, Langlois T, Wichert A. Flexible kernels for RBF networks[J]. Neurocomputing, 2006, 69(16-18): 2356-2359.
[179] Zhang H H. Variable selection for support vector machines via smoothing spline ANOVA[J]. Statictica Sinica, 2006, 16(2): 659-674.
[180] Chen Z, Li J P, Wei L W. A multiple kernel support vector machine scheme for feature selection and rule extraction from gene expression data of cancer tissue[J]. Artificial Intelligence in Medicine, 2007, 41(2): 161-175.
[181] Liu Y H, Chen Y T. Face recognition using total margin-based adaptive fuzzy support vector machines[J]. IEEE Transactions on Neural Networks, 2007, 18(1): 178-192.
[182] Wang D F, Yeung D S, Tsang E C C. Weighted Mahalanobis distance kernels for support vector machines[J]. IEEE Transactions on Neural Networks, 2007, 18(5): 1453-1461.
[183] Xu M H,Thompson P M, Toga A W. Adaptive reproducing kernel particle method for extraction of the cortical surface[J]. IEEE Transactions on Medical Imaging, 2006, 25(6): 755-767.
[184] Xu J W, Pokharel P P, Jeong K H, et al. An explicit construction of a reproducing Gaussian kernel Hilbert space[C]. ICASSP 2006 Proceedings, 2006: 573-576.
[185] Wu Q, Ying Y M, Zhou D X. Multi-kernel regularized classifiers[J]. Journal of Complexity, 2007, 23(1): 108-134.
[186] Zavidovique B, Gesu V D. The S-kernel: A measure of symmetry of objects[J]. Pattern Recognition, 2007, 40(3): 839-852.
[187]陈渤,刘宏伟,保铮等.一种针对高分辨雷达距离像识别的融合核优化算法[J].电子学报, 2006, 34(6): 1146-1151.
[188] Perez-Cruz F, Bousquet O. Kernel Methods and their potential use in signal processing[J]. IEEE Signal Processing Magazine, 2004, 5: 57-65.
[189]张铃.基于核函数的SVM机与三层前向神经网络的关系[J].计算机学报, 2002, 25(7): 696-700.
[190]范红旗.主动寻的制导中机动目标运动模式辨识技术[D].长沙:国防科技大学博士学位论文, 2008.
[191]周剑雄.光学区雷达目标三维散射中心重构理论及技术[D].长沙:国防科技大学博士学位论文, 2006.
[2] Huynen J R. Phenomenological theory of radar targets[D]. Delft, The Netherlands: Technical University of Delft, 1970.
[3] van Zyl J J. Unsupervised classification of scattering behavior using radar polarimetry data[J]. IEEE Transactions on Geoscience and Remote Sensing, 1989, 27(1): 36-45.
[4] Krogager E. New decomposition of the radar target scattering matrix[J]. Electronics Letters, 1990, 26(18): 1525-1527.
[5] Pottier E, Cloude S R. Application of the H/A/αpolarimetric decomposition theorems for land classification[C]. Proceedings of SPIE Conference on Wideband Interferometric Sensing and Imaging Polarimetry, San Diego, CA, USA, 1997: 132-143.
[6] Cloude S R, Pottier E. An entropy based classification scheme for land applications of polarimetric SAR[J]. IEEE Transactions on Geoscience and Remote Sensing, 1997, 35(1): 549-557.
[7] Lee J S, Grunes M R, Ainsworth T L, et al. Unsupervised classification using polarimetric decomposition and the complex Wishart classifier[J]. IEEE Transactions on Geoscience and Remote Sensing, 1999, 37(5): 2249-2258.
[8] Yang J, Peng Y N, Lin S M. Similarity between two scattering matrices[J]. Electronic Letters, 2001, 37(3): 193-194.
[9]徐俊毅,杨健,彭应宁.双波段极化雷达遥感图像分类的新方法[J].中国科学E辑, 2005, 35(10): 1083-1095.
[10] Berizzi F, Martorella M, Capria A. H/αpolarimetric features for man-made target classification[C]. IEEE Radar Conference, Rome, Italy, 2008: 1596-1601.
[11] Kostinski A B, Boerner W M. On foundations of radar polarimetry[J]. IEEE Transactions on Antennas and Propagation, 1986, 34(12): 1395-1404
[12] Vapnik V N. The nature of statistical learning theory[M]. New York: Springer, 2000.
[13]王雪松.宽带极化信息处理的研究[D].长沙:国防科技大学博士学位论文, 1999.
[14]吴永辉.极化SAR图像分类技术研究[D].长沙:国防科技大学博士学位论文, 2007.
[15]刘秀清.全极化合成孔径雷达极化信息处理技术研究[D].北京:中国科学院电子学研究所博士学位论文, 2004.
[16]周晓光.极化SAR图像分类方法研究[D].长沙:国防科技大学博士学位论文, 2008.
[17] Li H J, Wang Y D, Wang L H. Matching score properties between range profiles of high-resolution radar targets[J]. IEEE Transactions Antennas and Propagation, 1996, 44(4): 444-452.
[18] Cohen M N. Variability of ultra-high range resolution radar profiles and some implications for target recognition[C]. Proceedings of SPIE Conference on Signal Processing, Sensor Fusion and Target Recognition, 1992, 1699: 256-266.
[19]闫锦.基于高距离分辨像的雷达目标识别研究[D].北京:中国航天第二研究院博士学位论文, 2004.
[20] Cameron W L, Leung L K. Feature motivated polarization scattering matrix decomposition[C]. Proceedings of IEEE International Radar Conference, Arlington, VA, May 7-10, 1990: 549-557.
[21]李鸣.毫米波频率步进雷达前端关键技术研究[D].南京:南京理工大学博士学位论文, 2007.
[22]龙腾,毛二可,何佩琨.调频步进雷达信号分析与处理[J].电子学报, 1998, 26(12): 84-88.
[23]孙即祥.现代模式识别[M].长沙:国防科技大学出版社, 2002.
[24]杜兰.雷达高分辨距离像目标识别方法研究[D].西安:西安电子科技大学博士学位论文, 2007.
[25]胡磊.基于粗糙集理论的雷达目标高分辨距离像识别[D].长沙:国防科技大学硕士学位论文, 2008.
[26] Theodoridis S, Koutroumbas K著,李晶皎,朱志良,王爱侠等译.模式识别(第2版)[M].北京:电子工业出版社, 2004.
[27]熊洪允,曾绍标,毛云英著.应用数学基础[M].天津:天津大学出版社, 1998.
[28] Elizondo D. The linear separability problem: some testing methods[J]. IEEE Transactions on Neural networks, 2006, 17(2): 330-344.
[29] Ben-Israel A, Levin Y. The geometry of linear separability in data sets[J]. Linear Algebra and its Applications 2006, 416(1): 75-87.
[30] Pranckevicience E, Ho T K, Somorjai R. Class separability in space reduced by feature selection[C]. The 18th International Conference on Pattern Recognition (ICPR’06), Hong Kong, 2006, Vol.3: 254-257.
[31]吴涛.核函数的性质、方法及其在障碍检测中的应用[D].长沙:国防科技大学博士学位论文, 2003.
[32] Tax D M J, Duin R P W. Support vector data description [J]. Machine Learning, 2004, 54(1): 45-66.国防科学技术大学研究生院博士学位论文
[33]吴翊,屈田兴.应用泛函分析[M].长沙:国防科技大学出版社, 2002: 52-59.
[34]张铃.支持向量机理论与基于规划的神经网络学习算法[J].计算机学报, 2001, 24(2): 113-118.
[35] Cortes C, Vapnik V N. Support vector networks [J]. Machine Learning, 1995, 20(3): 273-297.
[36] Shawe-Taylor J, Cristianini N. Kernel methods for pattern analysis [M]. UK: Cambridge University Press, 2004.
[37]刘向东,骆斌,陈兆乾.支持向量机最优模型选择的研究[J].计算机研究与发展, 2005, 42(4): 576-581.
[38] R?tsch G. Benchmarks data sets. http://ida.first.fraunhofer.de/projects/bench /benchmarks.htm. 2003.7
[39] R?tsch G, Ondda T, Muller K R. Soft margins for AdaBoost[J]. Machine Learning, 2001, 42(3): 287-320.
[40] Hastie T, Rosset S, Tibshirani R, et al. The entire regularization path for the support vector machine [J]. Journal of Machine Learning Research, 2004, 5(12): 1391-1415.
[41] Lee M S, Keerthi S S, Ong C J, et al. An efficient method for computing leave-one-out error in support vector machines with Gaussian kernels[J]. IEEE Transactions on Neural Networks, 2004, 15(3): 750-757.
[42] Xu P, Chan A K. An efficient algorithm on multi-class support vector machine model selection[C]. Proceedings of the International Joint Conference on Neural Networks 2003. Portland, IEEE, 2003: 3229-3232.
[43] Chapelle O, Vapnik V N, Bousquet O, et al. Choosing multiple parameters for support vector machines[J]. Machine Learning, 2002, 16(1): 131-159.
[44] Keerthi S S. Efficient tuning of SVM hyper parameters using radius/margin bound and iterative algorithms[J]. IEEE Transactions on Neural Networks, 2002, 13(5): 1225-1229.
[45] Musicant D R, Kumar V, Ozgur A. Optimizing F-measure with support vector machines[C]. In the Sixteenth International Florida Artificial Intelligence Research Society Conference, St. Augustine, Florida, USA, 2003: 356-360.
[46] Agarwal R, Joshi M V. PNrule: a new framework for learning classifier models in data mining (a case-study in network intrusion detection)[C]. Proceedings of First SIAM International Conference on Data Mining, 2001: 1-30.
[47]李敏强,寇纪淞,林丹.遗传算法的基本理论与应用[M].北京:科学出版社,2002.
[48]刘守生.遗传算法与小波神经网络中若干问题的研究[D].南京:南京航空航天大学博士学位论文, 2004.
[49]李良敏,温广瑞,王生昌.基于遗传算法的改进径向基支持矢量机及其应用[J].系统仿真学报, 2008, 20(22): 6088-6092.
[50] Cortes C, Vapnik V N. Support vector networks[J]. Machine Learning, 1995, 20(3): 273-297.
[51] Morik K, Brockhausen P, Joachims T. Combining statistical learning with a knowledge-based approach- a case study in intensive care monitoring[C]. Proceedings of the 16th International Conference on Machine Learning. San Mateo, Canada: Morgan Kaufman Publishers, 1999: 268-277.
[52] Takuya I, Shigeo A. Fuzzy support vector machine for pattern classification[C]. Proceeding of International Joint Conference on Neural Networks, Washington, D.C, 2001: 1449-1454.
[53] Bo L F, Wang L, Jiao L C. Multiple parameter selection for LS-SVM using smooth leave-one-out error[J]. Lecture Notes in Computer Science, Berlin: Springer, 2005: 851-856.
[54] Peng X Y, Wu H X, Peng Y. Parameter selection method for SVM with PSO[J]. Chinese Journal of Electronics, 2006, 15(4): 638-642.
[55] Bi L P, Huang H, Zheng Z Y. New heuristic for determination Gaussian kernel parameter[C]. Proceedings of the Fourth International Conference on Machine Learning and Cybernetics, Guangzhou, 2005: 4299-4304.
[56] Eitrich T, Lang B. Efficient Optimization of support vector machine learning parameters for unbalanced datasets[J]. Journal of computational and applied mathematics, 2006, 196(2): 425-436.
[57] Lee M S, Keerthi S S. An efficient method for computing Leave-One-Out error in Support Vector Machines with Gaussian kernels[J]. IEEE Transactions on Neural Networks, 2004, 15(3): 750-757.
[58] Jaakkola T S, Haussler D. Probabilistic kernel regression models[C]. Proceedings of the 1999 Conference on AI and Statistics, 1999.
[59] Opper M, Winther O. Gaussian process and SVM: Mean field and Leave-One- Out[J]. Advances in Large Margin Classifiers, MIT Press, 2000: 311-326.
[60] Chung K M, Sun C L. Radius margin bounds for Support Vector Machines with RBF kernel[C]. Proceedings of the 9th International Conference on Neural Information, 2002, 2: 893-897.
[61] Chung K M, Kao W C, Sun C L. Radius margin bounds for Support Vector Machines with RBF kernel[J]. Neural Computing, 2003, 15(11): 2643-2681.
[62] Vapnik V N, Chapelle O. Bounds on error expectation for SVM[J]. Advances in Large Margin Classifiers, MIT Press, 2000: 261-280.
[63]郑松峰.支撑向量机的模型和参数选择研究[D].西安:西安交通大学硕士学位论文, 2003.
[64]沈明华.散射中心分布特征提取与核方法分类器关键技术研究[D].长沙:国防科技大学博士学位论文, 2007.
[65] Lanckriet G R G, Cristianini N, Bartlett P, Ghaoui L E, et al. Learning the kernel matrix with semi-definite programming[J]. Journal of Machine Learning Research, 2004, 5(12): 27-72.
[66] Ayatt N E, Cheriet M, Suen C Y. Automatic model selection for the optimization of SVM kernels[J]. Pattern Recognition, 2005, 38(10): 1733-1745.
[67] Friedrichs F, Igel C. Evolutionary tuning of multiple SVM parameters[J]. Neural Computing, 2005, 64(3): 107-117.
[68] Schittkowski K. Optimal parameter selection in support vector machines[J]. Journal of Industrial and Management Optimization, 2005, 1(4): 465-476.
[69] Gold C, Holub A, Sollich P. Bayesian approach to feature selection and parameter tuning for support vector machine classifiers[J]. Neural Networks, 2005, 18(5-6): 693-701.
[70] Rennie J D M. Derivation of the F-measure[EB/OL]. http://people.csail.mit.edu/ ~jrennie/writing, 2004.
[71] Abouzakhar N S, Manson G A. Evaluation of intelligent intrusion detection models[J]. International Journal of Digital Evidence, 2004, 3(1): 1-20.
[72] Sch?lkopf B, Smola A, Müller K R. Nonlinear component analysis as a kernel eigenvalue problem[J]. Neural Computation, 1998, 10(5): 1299-1319.
[73] Xu Y, Zhang D, Song FX, et al. A method for speeding up feature extraction based on KPCA[J]. Neural Computing, 2007, 70(4-6): 1056-1061.
[74] Liu Y H, Chen Y T, Lu S S. Face detection using kernel PCA and imbalanced SVM[J]. Lecture Notes in Computer Science, Berlin: Springer-Verlag, 2006, 4221: 351-360.
[75] Li W H, Gong WG, Liang Y X, et al. Feature selection based on KPCA, SVM and GSFS for face recognition[J]. Lecture Notes in Computer Science, Berlin: Springer-Verlag, 2005, 3687: 344-350.
[76] Gu Y F, Zhang Y. Hyperspectral small target detection by combining kernel PCA with linear mixture model[J]. Chinese Journal of Electronics, 2005, 14(1): 130-134.
[77] Mika S, R?tsch G, Weston J, et al. Fisher discriminant analysis with kernels[J]. Neural Networks for Signal Processing IX. New York: IEEE Press, 1999: 41-48.
[78] Zhang J F, Huang Z C. Novelty detection methods and novel fault class detection[C]. 1ST International Symposium on Digital Manufacture, 2006, 1-3: 767-771.
[79] Lee S W, Park J Y, Lee S W. Low resolution face recognition based on support vector data description[J]. Pattern Recognition. 2006, 39(9): 1809-1812.
[80] Park J, Kang D, Kwok J T, et al. Facial image reconstruction by SVDD-basedpattern de-noising[J]. Lecture Notes in Computer Science, Berlin: Springer- Verlag, 2006, 3832: 129-135.
[81] Pan M Q, Qian S X, Lei L Y, et al. Support vector data description with model selection for condition monitoring[C]. Proceedings of 2005 International Conference on Machine Learning and Cybernetics, 2005, 1: 4315-4318.
[82]郭桂蓉,庄钊文.信息处理中的模糊技术[M].长沙:国防科技大学出版社, 1993.
[83]肖怀铁.宽带极化毫米波雷达目标特征信号测量与识别算法研究[D].长沙:国防科技大学博士学位论文, 2000.
[84] Blake C L, Merz C J. UCI repository of learning database[DB/OL], Available at http://www.ics.uci.edu/~mlearn/MLRepository.html, 1998.
[85] Takuya I, Shigeo A. Fuzzy Support Vector Machine for pattern classification[C]. Proceedings of International Joint Conference on Neural Networks, Washington, D.C, 2001, 7: 1449-1454.
[86] Daisuke T, Shigeo A. Fuzzy Least Squares Support Vector Machines for multiclass problems[J]. Neural Networks, 2003, 16(5-6): 785-792.
[87] Lin C F, Wang S D. Fuzzy support vector machines[J]. IEEE Transactions on Neural Networks, 2002, 13(2): 464-471.
[88] Cristianini N, Shawe-Taylor J著,李国正,王猛,曾华军译.支持向量机导论[M].北京:电子工业出版社, 2006.
[89] Peng X Y, Wu H X, Peng Y. Parameter selection method for SVM with PSO[J]. Chinese Journal of Electronics, 2006, 15(4): 638-642.
[90] Bennett K, Champbell O. Support vector machines: hyper or hallelujah?[J]. SIGKDD Explorations, 2000, 2(2): 1-13.
[91]陶卿,孙德敏,范劲松.基于闭凸包收缩的最大边缘线性分类器[J].软件学报, 2002, 13(3): 404-409.
[92] Kang W S, Ki H I, Jin Y C. SVDD-based method for fast training of multi-class Support Vector Classifier[J]. Lecture Notes on Computer Science, Berlin: Springer-Verlag, 2006, 3971: 991-996.
[93]孙文峰.雷达目标识别技术述评[J].雷达与对抗, 2001(3): 1-8.
[94]庄钊文,肖顺平,王雪松.雷达极化信息处理及应用[M].北京:国防工业出版社,1999.
[95]代大海.极化雷达成像及目标特征提取研究[D].长沙:国防科技大学博士学位论文, 2008.
[96]王涛.弹道中段目标极化域特征提取与识别[D].长沙:国防科技大学博士学位论文, 2006.
[97]李永祯,王雪松,徐振海等.基于强散射点径向积累的高分辨极化目标检测研究[J].电子学报, 2001, 29(3): 307-310.
[98]曾勇虎.极化雷达时频分析与目标识别的研究[D].长沙:国防科技大学博士学位论文, 2004.
[99]代大海,王雪松,肖顺平.基于二维CP-GTD模型的全极化ISAR超分辨成像[J].自然科学进展. 2007, 17(9): 131-140.
[100] Steadly W M, Moses R L. High resolution exponential modeling of fully polarized radar returns[J]. IEEE Transactions on Aerospace and Electronic Systems, 1991, 27(3): 459-468.
[101]庄钊文,黎湘,刘永祥.智能化武器系统发展的关键技术—雷达自动目标识别技术[J].科技导报, 2005, 23(8): 20-23.
[102] Chamberlain N F, Walton E K Garber F D. Radar target identification of aircraft using polarization diverse features[J]. IEEE Transactions on Aerospace and Electronic Systems, 1991, 27(1): 58-67.
[103]陈曾平.雷达目标结构特征识别的理论与应用[D].长沙:国防科技大学博士学位论文, 1994.
[104]郭桂蓉,庄钊文,陈曾平.电磁特征抽取与目标识别[M].长沙:国防科技大学出版社, 1995.
[105]何松华,郭桂蓉,庄钊文.雷达目标高分辨率距离-极化结构成像方法研究[J].电子学报, 1994, 22(7): 1-7.
[106]何松华.高距离分辨力毫米波雷达目标识别的理论与应用[D].长沙:国防科技大学博士学位论文, 1993.
[107]肖顺平,郭桂蓉,庄钊文,王雪松.基于极化谱的飞机目标识别[J].电子学报, 1997, 25(12): 60-64.
[108]肖顺平,王雪松,庄钊文.基于极化不变量的飞机目标识别[J].红外与毫米波学报,1996, 15(6): 439-444.
[109]肖顺平,郭桂蓉,王雪松.基于极化频率稳定度的目标识别[J].现代雷达, 1995, 17(5): 1-7.
[110]李盾,肖顺平,王雪松.基于回波趋向伪本征极化特性的目标识别研究[J].电子学报, 1999, 27(9): 1-4.
[111] Ferrazzoli P, Paloscia S, Pampaloni P, et al. The potential of multifrequency polarimetric SAR in assessing agricultural and arboreous biomass[J]. IEEE Transactions on Geoscience and Remote Sensing, 1997, 35(1): 5-17.
[112] Ferrazzoli P, Guerriero L, Schiavon G. Experimental and model investigation on radar classification capability[J]. IEEE Transactions on Geoscience and Remote Sensing, 1999, 37(2): 960-968.
[113] Pellizzeri T M, Lombardo P. Model-based processing of multifrequencypolarimetric SAR images of urban areas[C]. Proceedings of GRSS/ISPRS Joint Workshop on Data Fusion and Remote Sensing over Urban Areas, 2003: 47-51.
[114] Lombardo P. Optimal classification of polarimetric SAR images using segmentation[C]. Proceedings of IEEE International Radar Conference, Long Beach, California, 2002: 8-13.
[115] Lee J S, Krogager E, Ainsworth T L, et al. Polarimetric analysis of radar signature of a manmade structure[J]. IEEE Transactions on Geoscience and Remote Sensing letters, 2006, 3(4): 555-559.
[116] Karnychev V, Valery A K, Leo P L, et al. Algorithms for estimating the complete group of polarization invariants of the scattering matrix (SM) based on measuring all SM elements[J]. IEEE Transactions on Geoscience and Remote Sensing, 2004, 42(3): 529-539.
[117]张澄波.综合孔径雷达原理、系统分析与应用[M].北京:科学出版社, 1989.
[118]李盾,肖顺平,王雪松等.基于极化度分布特征的目标识别方法研究[J].电子科学学刊, 2000, 22(6): 994-1000.
[119] Dong Y H, Forster B C, Ticehurst C. A new decomposition of radar polarization signatures[J]. IEEE Transactions on Geoscience and Remote Sensing, 1998, 36(3): 933-939.
[120] Seisuke F, Haruto H. A wavelet-based texture feature set applied to classification of multifrequency polarimetric SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 1999, 37(5): 2282-2286.
[121] Khan K U, Yang J. Novel features for polarimetric SAR image classification by neural network[C]. Proceedings of International Conference on Neural Networks and Brain, 2005: 165-170.
[122] Cloude S R, Pottier E. A review of target decomposition theorems in radar polarimetry[J]. IEEE Transactions on Geoscience and Remote Sensing, 1996, 34(2): 498-518.
[123] Touzi R, Raney R K, Charbonneau F. On the use of permanent symmetric scatterers for ship characterization[J]. IEEE Transactions on Geoscience and Remote Sensing, 2004, 42(10): 2039-2045.
[124] van Zyl J J. Unsupervised classification of scattering behavior using radar polarimetry data[J]. IEEE Transactions on Geoscience and Remote Sensing, 1989, 27(1): 36–45.
[125] Freeman A, Durden S. Three-component scattering model to describe polarimetric SAR data[C]. Proceedings of SPIE Radar Polarimetry Conference, San Diego, CA, 1992: 213–224.
[126] Dong Y H, Forster B C, Ticehurst C. A new decomposition of radar polarization signatures[J]. IEEE Transactions on Geoscience and Remote Sensing, 1998,36(3):933-939.
[127] Krogager E. Decomposition of the radar target scattering matrix with application to high resolution target imaging[C]. Proceedings of IEEE National Telesystems Conference (NTC’91), Atlanta, GA, USA, 1991: 77-82.
[128] Krogager E. Wideband Pol-SAR/ISAR signal and image processing for target identification and discrimination[C]. Proceedings of IEEE Asia-Pacific Microwave Conference, Adelaide, Melbourne, Australia, 1992: 947-950.
[129]李莹,任勇,山秀明.基于目标分解的极化雷达飞机识别法[J].清华大学学报(自然科学版), 2001, 41(7): 32-35.
[130] Cameron W L, Youssef N N, Leung L K. Simulated polarimetric signatures of primitive geometrical shapes[J]. IEEE Transactions on Geoscience and Remote Sensing, 1996, 34(3): 793-803.
[131] Touzi R, Charbonneau F. Characterization of Target Symmetric Scattering Using Polarimetric SARs[J]. IEEE Transactions on Geoscience and Remote Sensing, 2002, 40(11): 2507-2516.
[132] Touzi R, Raney R K, Charbonneau F. On the use of permanent symmetric scatterers for ship characterization[J]. IEEE Transactions on Geoscience and Remote Sensing, 2004, 42(10): 2039-2045.
[133] Freeman A, Durden S L. A three-component scattering model for polarimetric SAR data[J]. IEEE Transactions on Geoscience and Remote Sensing, 1998, 36(3): 963-973.
[134] Yamaguchi Y, Moriyama T, Ishido M, et al. Four-component scattering model for polarimetric SAR image decomposition[J]. IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(8): 1699-1706.
[135] Yamaguchi Y, Yajima Y, Yamada H. A four-component decomposition of POLSAR images based on the coherency matrix[J]. IEEE Transactions on Geoscience and Remote Sensing Letters, 2006, 3(3): 292-296.
[136] Holm W A, Barnes R M. On radar polarization mixed target state decomposition techniques[C]. Proceedings of IEEE National Radar Conference, April 1988: 249-254.
[137] Ferro-Famil L, Pottier E, Lee J S. Unsupervised classification of multifrequency and fully polarimetric SAR images based on the H/A/Alpha-Wishart classifier[J]. IEEE Transactions on Geoscience and Remote Sensing, 2001, 39(11): 2332-2342.
[138] Lopez-Sanchez J M, Fortuny-Guasch J, Sieber A J. Experimental validation of an entropy-based classification scheme using a wide-band polarimetric radar[C]. Proceedings of International Geoscience and Remote Sensing Symposium, Seattle, Washington, USA, July 1998: 2381-2383.
[139] Park S E, Moon W M. Classification of the polarimetric SAR using fuzzy boundaries in entropy and alpha plane[C]. Proceedings of International Geoscience and Remote Sensing Symposium, Seoul, Korea, July 2005: 5517-5519.
[140] Lombardo P, Pellizzeri T M, Tomasuolo A. A joint classification technique based on multivariate annealing segmentation and "H/A/α" decomposition for fully polarimetric SAR images of suburban areas[C]. Proceedings of International Geoscience and Remote Sensing Symposium, Sydney, Australia, July 2001: 2922-2924.
[141] Praks J, Hallikainen M. A novel approach in polarimetric covariance matrix eigen-decomposition[C]. Proceedings of International Geoscience and Remote Sensing Symposium, Honolulu, Hawaii, USA, July 2000: 1119-1121.
[142]高明星,杨健,彭应宁.极化雷达遥感中目标特征提取[J],电波科学学报, 2004, 19(4): 418-421.
[143]王文光,王俊,毛士艺等.基于极化SAR图像分类的海上舰船目标检测[J].信号处理, 2007, 23(5):676-679.
[144]范立生,高明星,杨健等.极化SAR遥感中森林特征的提取[J].电波科学学报, 2005, 20(5): 553-556.
[145]董贵威,杨健,彭应宁等.极化SAR遥感中森林特征探测[J].清华大学学报(自然科学版), 2003, 43(7): 953-956.
[146]袁莉.基于高分辨距离像的雷达目标识别方法研究[D].西安:西安电子科技大学博士学位论文, 2007.
[147]廖学军.基于高分辨距离像的雷达目标识别[D].西安:西安电子科技大学博士学位论文, 1999.
[148]杜兰,保铮,邢孟道.飞机目标的雷达一维距离像特性研究[J].西安电子科技大学学报, 2002, 28(sup.): 14-19.
[149]杜兰,刘宏伟,保铮.利用目标方位信息改善雷达距离像识别性能[J].系统工程与电子技术. 2004, 26(8): 1040-1043.
[150]裴炳南.高分辨雷达自动目标识别方法研究[D].西安:西安电子科技大学博士学位论文, 2002.
[151]袁莉,刘宏伟,保铮.基于中心矩特征的雷达HRRP自动目标识别[J].电子学报, 2004, 32(12): 2078-2081.
[152]杜兰,刘宏伟,保铮.一种利用雷达目标高分辨距离像幅度起伏特性的特征提取新方法[J].电子学报, 2005, 33(3): 411-415.
[153] Cristianini N, Shawe-Taylor J. An introduction to support vector machines and other kernel-based learning methods[M]. UK, Cambridge: Cambridge UniversityPress, 2000.
[154]高学,金连文.一种基于支持向量机的手写汉字识别方法[J],电子学报, 2002, 30(5): 651-654.
[155]周志明,王以志,黄文芝等.基于小波和支持向量机的人脸识别技术[J].计算机工程与应用, 2004, 40(12): 52-54.
[156]朱永生.支持向量机及其在机械故障模式识别中的应用[D].西安:西安交通大学博士学位论文, 2003.
[157] Bruzzone L, Melgani F. Classification of hyperspectral images with support vector machines: multiclass strategies[C]. Proceedings of SPIE Image and Signal Processing for Remote Sensing, Bellingham, WA, 2004, 5238: 408-419.
[158] Serpico S B, Bruzzone L. A new search algorithm for feature selection in hyperspectral remote sensing images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2001, 39(7): 1360-1367.
[159] Brown M, Lewis H G, Gunn S R. Linear spectral mixture models and support vector machines for remote sensing[J]. IEEE Transactions on Geoscience and Remote Sensing, 2000, 38(5): 2346 -2360.
[160] Waagen D, Cassabaum M, Schmitt H, et al. Support vector machine optimization via margin distribution analysis[C]. Proceedings of SPIE Automatic Target Recognition, 2003, 5094: 348-357.
[161] Ma J S, Li H L, Ahalt S C. Using support vector machines as HRR signature classifiers[C]. Proceedings of SPIE Automatic Target Recognition, 2004, 4379: 209-215.
[162] Samanta B. Gear fault detection using artificial neural networks and support vectormachines with genetic algorithms[J]. Mechanical Systems and Signal Processing, 2004, 18: 625-644.
[163]张莉.支撑矢量机与核方法研究[D].西安:西安电子科技大学博士学位论文, 2002.
[164]周伟达.核机器学习方法研究[D].西安:西安电子科技大学博士学位论文, 2003.
[165] Peng X Y, Wu H X, Peng Y. Parameter selection method for SVM with PSO[J]. Chinese Journal of Electronics, 2006, 15(4): 638-642.
[166] Cover T M. Geometrical and statistical properties of systems of linear inequalities with applications in pattern recognition[J]. IEEE Transactions on Electronic Computers, 1965, 14(4): 326-334.
[167] Haykin S著,叶世伟,史忠植译.神经网络原理[M].北京:机械工业出版社, 2004.
[168] Tao X M, Liu F R, Zhou T X. A novel approach to intrusion detection based onsupport vector data description[C]. The 30th Annual Conference of the IEEE Industrial Electronics Society, Busan, Korea, November, 2004: 2016-2021.
[169] Banerjee A, Burlina P, Diehl C. A support vector method for anomaly detection in hyperspectral imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2006, 44(8): 2282-2291.
[170] Lee K Y, Kim D W, Lee K H, et al. Density-induced support vector data description[J]. IEEE Transactions on Neural Networks Letters, 2007, 18(1): 284-289.
[171] Sanchez-Hernandez C, Boyd D S, Foody G M. One class classification for mapping a specific land-cover class: SVDD classification of Fenland[J]. IEEE Transactions on Geoscience and Remote Sensing, 2007, 45(4): 1061-1072.
[172]陆从德,张太镒,胡金燕.基于乘性规则的支持向量域分类器[J].计算机学报, 2004, 27(5): 690-694.
[173]吴涛,贺汉根,贺明科.基于插值的核函数构造[J].计算机学报, 2003, 26(8): 990-996.
[174] Amari S, Wu S. Improving support vector machine classifiers by modifying kernel functions[J]. Neural Networks, 1999, 12(6): 783–789.
[175] Xiong H L, Swamy M N S, Omair-Ahmad M. Optimizing the kernel in the empirical feature space[J]. IEEE Transactions on Neural Networks, 2005, 16(2): 460-474.
[176] Micchelli C A,Pontil M. Learning the kernel function via regularization[J]. Journal of Machine Learning Research, 2005, 6(7): 1099-1125.
[177] Zhang M, Zhou G D, Aiti A. Exploring syntactic structured features over parse trees for relation extraction using kernel methods[J]. Information Processing and Management, 2007, 43(4): 969-982.
[178] Falcao A O, Langlois T, Wichert A. Flexible kernels for RBF networks[J]. Neurocomputing, 2006, 69(16-18): 2356-2359.
[179] Zhang H H. Variable selection for support vector machines via smoothing spline ANOVA[J]. Statictica Sinica, 2006, 16(2): 659-674.
[180] Chen Z, Li J P, Wei L W. A multiple kernel support vector machine scheme for feature selection and rule extraction from gene expression data of cancer tissue[J]. Artificial Intelligence in Medicine, 2007, 41(2): 161-175.
[181] Liu Y H, Chen Y T. Face recognition using total margin-based adaptive fuzzy support vector machines[J]. IEEE Transactions on Neural Networks, 2007, 18(1): 178-192.
[182] Wang D F, Yeung D S, Tsang E C C. Weighted Mahalanobis distance kernels for support vector machines[J]. IEEE Transactions on Neural Networks, 2007, 18(5): 1453-1461.
[183] Xu M H,Thompson P M, Toga A W. Adaptive reproducing kernel particle method for extraction of the cortical surface[J]. IEEE Transactions on Medical Imaging, 2006, 25(6): 755-767.
[184] Xu J W, Pokharel P P, Jeong K H, et al. An explicit construction of a reproducing Gaussian kernel Hilbert space[C]. ICASSP 2006 Proceedings, 2006: 573-576.
[185] Wu Q, Ying Y M, Zhou D X. Multi-kernel regularized classifiers[J]. Journal of Complexity, 2007, 23(1): 108-134.
[186] Zavidovique B, Gesu V D. The S-kernel: A measure of symmetry of objects[J]. Pattern Recognition, 2007, 40(3): 839-852.
[187]陈渤,刘宏伟,保铮等.一种针对高分辨雷达距离像识别的融合核优化算法[J].电子学报, 2006, 34(6): 1146-1151.
[188] Perez-Cruz F, Bousquet O. Kernel Methods and their potential use in signal processing[J]. IEEE Signal Processing Magazine, 2004, 5: 57-65.
[189]张铃.基于核函数的SVM机与三层前向神经网络的关系[J].计算机学报, 2002, 25(7): 696-700.
[190]范红旗.主动寻的制导中机动目标运动模式辨识技术[D].长沙:国防科技大学博士学位论文, 2008.
[191]周剑雄.光学区雷达目标三维散射中心重构理论及技术[D].长沙:国防科技大学博士学位论文, 2006.