支持向量机算法研究及在气象数据挖掘中的应用
|
摘要
针对气象数据具有时空属性、多维、复杂、相关度高等问题,提出基于SVM解决时序多维气象预测的方法。在进行气象数据挖掘时,预报国子的选择和处理是决定预测精度和有效性的关键所在。本文针对基于精度的分类算法在不平衡数据挖掘中表现出有偏性问题,提出解决类不平衡挖掘的补偿方式和重构样本集的SVM算法,将噪声代价嵌入SVR模型中,有效减小噪音数据引起的回归模型的过学习问题。本文给出气象数据挖掘中,选择预报国子的基本方法和处理原则,同时对大规模数据的处理和分类问题、高维特征数据的特征约简和特征优选、SVM核参数的选择和优化、以及采用增量学习算法增强模型的自适应能力等问题进行了探讨和研究。实验结果表明,基于SVM的气象数据挖掘具有较高的预测正确性。
This paper proposes a method for weather forecasting of multi-dimensional, time-series meteorological data based on SVM algorithm, which could effectively solve the troublesome problems of meteorological data mining, as the attributes of meteorological data have the characters of multi-dimension, complex, high dependency and correlation, also concerning with time and space factors. The key point to obtain the satisfactory accuracy and effectiveness of meteorological data mining is to effectively select and correctly process the forecasting factors. This paper proposes an approach to compensate the imbalance of classes and to reconstruct training sample set for SVM, which could solve the bias problem of the classification algorithms. With the cost factor of noise data embedded into SVR model, the SVR algorithm obtains the effective regression result in solving problem of overfitting. This paper researches and discusses on the basic methods and principles of selecting factors, processing and classification of large-scale data, reduction and optimization of high dimensional quantization factors, selection and optimization of SVM kernel function's parameters, and the incremental learning SVM algorithm. The experiments have showed that meteorological data mining based on SVM has satisfactory performance and efficiency.
This paper proposes a method for weather forecasting of multi-dimensional, time-series meteorological data based on SVM algorithm, which could effectively solve the troublesome problems of meteorological data mining, as the attributes of meteorological data have the characters of multi-dimension, complex, high dependency and correlation, also concerning with time and space factors. The key point to obtain the satisfactory accuracy and effectiveness of meteorological data mining is to effectively select and correctly process the forecasting factors. This paper proposes an approach to compensate the imbalance of classes and to reconstruct training sample set for SVM, which could solve the bias problem of the classification algorithms. With the cost factor of noise data embedded into SVR model, the SVR algorithm obtains the effective regression result in solving problem of overfitting. This paper researches and discusses on the basic methods and principles of selecting factors, processing and classification of large-scale data, reduction and optimization of high dimensional quantization factors, selection and optimization of SVM kernel function's parameters, and the incremental learning SVM algorithm. The experiments have showed that meteorological data mining based on SVM has satisfactory performance and efficiency.
引文
[1]Andersson E., Pailleux J., Theaut J. N., et al. Use of cloud-cleared radiances in three/four dimensional variational data assimilation[J]. Q. J. R. Meteorol. Soc.,1994:120-125
[2]Bouttier F., Coutier, P.. Data assimilation and Concepts and methods[P]. ECMWF train notes. 1999.
[3]王跃山.客观分析和四维同化一站在新世纪的回望(Ⅱ)客观分析的主要方法(2)[J].气象科技.2001,Vol(3)：25-28.
[4]张卫民.气象资料变分同化的研究与并行计算实现[D].国防科学技术大学博士学位论文.2005
[5]Han J., Kamber M. Data Mining:Concepts & Techniques [M]. Morgan Kaufmann Publishers. 2001:30-33.
[6]朱明.数据挖掘[M].中国科技大学出版社.2002：35-36.
[7]Michael Steinbach, Pang-Ning Tan, Vipin Kumar, et al. Discovery of climate indices using clustering[C]. Proceedings of the ninth ACM SIGK.DD international conference on Knowledge discovery and data mining.2003.
[8]杨炳儒.数据挖掘领域的两大猜想.通讯和计算机.2007.Vol 4(4)：1-4,8.
[9]陈旭辉.沙尘暴资料的数据挖掘算法分析及系统实现[D].兰州大学硕士学位论文.2008
[10]马开玉,张耀辉,陈星.现代应用统计学[M].气象出版社.2003.
[11]王得民,施能.气象统计预报[M].气象出版社.1990.
[12]陈宝学,俞经善,关宏伟.数据挖掘技术应用于天气预报的可行性研究[J].应用科技.2004.Vol3：20-23.
[13]Xiang Li, Rahul Ramachandran, Sara Graves, et al. Automated detection of frontal systems from numerical model-generated data[C]. Proceeding of the eleventh ACM SIGKDD international conference On Knowledge discovery in data mining.2005:765-771.
[14]Yang Yubin, Lin Hui, Guo Zhongyang, et al. A meteorological conceptual modeling approach based on spatial data mining and knowledge discovery[C]. Proceedings of the 18th international conference on Innovations in Applied Artificial Intelligence.2005:342-346.
[15]Rie Honda, Shuai Wang, Tokio Kikuchi, et al. Mining of Moving Objects from Time-Series Images and its Application to Satellite Weather Imagery[J]. Journal of Intelligent Information Systems.2002. Vol19(1):79-93.
[16]Sherri K. Harms, Jitender S. Deogun. Sequential Association Rule Mining with Time Lags[J]. Journal of Intelligent Information Systems.2004. Vol22(1):7-22.
[17]Auroop R. Ganguly. A Hybrid Approach to Improving Rainfall Forecasts[J]. Computing in Science and Engineering.2002. Vol4(4):14-21.
[18]Jiang Zhao, Chang-Tien Lu, Yufeng Kou. Detecting region outliers in meteorological data[C]. Proceedings of the 11th ACM international symposium on Advances in geographic information systems.2003.
[19]Kenneth A. Hawick, P. D. Coddington, H.A. James. Distributed frameworks and parallel algorithms for processing large-scale geographic data[J]. Parallel Computing.2003. Vol29(10):1297-1333.
[20]Wallace J. M., Smith C., Bvetherton C. S. Singular value decomposition of winter time sea surface temperature and 500-mb height anomalies[J]. Journal of climate.1992. Vol5(6):561-576.
[21]Silverman D., Dracup J. Artificial network and long-range precipitation in California[J]. Journal of Applied Meteorology.2000. Vol31(1):57-66.
[22]文俊浩,向宏,李立新等.基于邻接关系的空间聚类算法研究[J].计算机工程与应用.2003.Vol39(34)：184-186.
[23]Sherri K. Harms, Jitender S. Deogun. Sequential Association Rule Mining with Time Lags[J]. Journal of Intelligent Information Systems.2004. Vol22(1):7-22.
[24]李娜娜,宋洁,顾军华等.灾害天气关联模式挖掘技术研究[J].河北工业大学学报.2005.Vol34(2)：68-73.
[25]McCann D.W. A neural network short-term forecast of significant thunderstorms[J]. Weather and Forecasting.1992. Vol7:525-534.
[26]Baik Longjin, Hwang Hongsub. Tropical cyclone intensity prediction using regression method and neural network[J]. Journal of the Meteorology Society of Japan.1998. Vol76(5):711-717.
[27]金龙,吴建生.基于遗传算法的神经网络短期预报预测模型[J].灾害学.2004.Vol8(1)：15-16.
[28]曹晓钟,刘还珠.神经网络在气象应用中的并行实现[J].计算机工程与应用.2003.Vol36：207-209.
[29]金龙,况雪源,黄海洪.人工神经网络预报模型的过拟合研究[J].气象学报.2004.Vol62(1)：62-70.
[30]张迎春,肖冬荣,赵远东.基于时间序列神经网络的气象预测研究[J].武汉大学学报(交通科学与工程版).2003.Vol27(2)：237-240.
[31]Rie Honda, Shuai Wang, Tokio Kikuchi,et al. Mining of Moving Objects from Time-Series Images and its Application to Satellite Weather Imagery[J]. Journal of Intelligent Information Systems.2002. Vol19(1):79-93.
[32]石扬,张燕平,赵妹等.基于商空间的气象时间序列数据挖掘研究[J].计算机工程与应用.2007.Vo143(1)：201-203.
[33]李集明,沈文海,王国复.气象信息共享平台及其关键技术研究[J].应用气象学报.2006.Vol17(5)：60-62.
[34]谭晓光.数据仓库技术在天气预报决策中的应用探讨[J].应用气象学报.2006.Vol 17(3)：325-332.
[35]刘伟,宋倩,陈佳.气象OLAP系统数据显示的解决方案[J].计算机工程与设计.2006.Vo127(3)：482-484.
[36]Biing T. Guana, Hsin-Wu Hsua, Tsong-Huei Weyb, et al. Modeling monthly mean temperatures for the mountain regions of Taiwan by generalized additive models[J]. Agricultural and Forest Meteorology.2009. Vol149(2):281-290
[37]Vapnik V.N. Statistical Learning Theory[M]. J.Wiley.1998.
[38]Boser B., Guyon I., Vapnik V.N. A training algorithm for optimal margin classifiers[C]. Fifth Annual Workshop on Computational Learning Theory. ACM Press.1992
[39]Scholkopf B., Burges C., Vapnik V.N. Extracting support data for a given task[C]. Proceedings of First International Conference on Knowledge Discovery and Data Mining, AAAI Press.1995:262-267.
[40]Guyon I., Boser B., Vapnik V.N. Automatic capacity turning of very large VC-dimension classifiers. Advances in Neural Information Processing Systerns[J]. Morgan Kaufmann.1993 Vol5:147-155.
[41]Cortes C., Vapnik V.N. Support vector networks[J]. Machine Learning.1995 Vol20:273-297.
[42]Vapnik V.N., Golowieh S., Smola A. Support vector method for function approximation, Regression Estimation, and Signal Processing. Advances in Neural Information Processing Systems[M]. Cambridge, MA.1997:281-287.
[43]邓乃扬,田英杰.数据挖掘中的最优化方法一支持向量机[M].科学出版社.2004.
[44]Cristianini N., Shawe-Yaylor J.,李国正等译.支持向量机导论[M].电子工业出版社.2004.
[45]Scholkopf B., Burges C., Vapnik V.N. Incorporating invariance in support vector learning machines. Artificial Neural Networks-ICANN'96, Lecture Notes in Computer Science, 1996(1112):47-52.
[46]Scholkopf B., Bartlett P., Smola A. et al. Support vector regression with automatic accuracy control. Proceeding of the 8th International conference on Artificial Neural Networks. 1998:111-116.
[47]Drucker H., Burges C., Kaufman L., et al. Support vector regression machines, Advances in Neural Information Processing Systems[M]. MITPress.1997:155-161.
[48]Pontil M., Rifkin R., Evgeniou T. From regression to classification in support machines. Proceedings ESANN.1999:225-230.
[49]Chang C., Lin C. Training v-support vector regression:theory and algorithms [J]. Neural Computation.2002. Vol14(8):1959-1977.
[50]Smola A.J., Scholkopf B., Muller K.R. The connection between regularization operators and support vector Kernels[J]. Neural Networks.1998. Vol11:637-649.
[51]Muller K.R., Smola A.J. Predicting time series with support vector machines[C]. Proceeding of the International Conference on Artificial Neural Networks.1997.999-1004.
[52]Mukherjee S., Osuna E., Girosi F. Nonlinear prediction of chaotic time series using a support vector machine[C]. Neural Networks for Signal Processing Ⅶ-Proceeding of the 1997 IEEE Workshop.1997.511-520.
[53]付岩,王耀威,王伟强等.SVM用于基于内容的自然图像分类和检索[J].计算机学报.2003.Vol26(10)：1261-1265.
[54]张宪,李晓娟.支持向量机在显微图像分类中的应用研究.计算机应用.2008.Vol28(3)：790-794.
[55]王新明,梁维泰,周方等.基于支持向量机和Getis因子的高分辨率遥感图像分类[J].地理与地理信息科学.2008.Vol24(4)：16-19,24.
[56]居红云,张俊本,李朝峰等.基于K-means与SVM结合的遥感图像全自动分类方法[J].计算机应用研究.2007.Vol24(11)：318-320.
[57]Brown M., Gunn S.R. Empirical data modelling algorithms:additive spline models
[58]and support vector machines[C]. Proc of UKACC International Conference on Control. 1998(1):709-714.
[59]王定成,方廷健,高理富等.支持向量机回归在线建模及应用[J].控制与决策.2003.Vol18(1)：89-91.
[60]张浩然,韩正之,李昌刚.基于支持向量机的非线性系统辨识[J].系统仿真学报.2003.Vol15(1)：119-121.
[61]Suykens A.K., Vandewalle J., Demoor B. Optimal control by least squares support vector machines[J]. Neural Networks.2001. Vol14:105-113.
[62]Kruif B.J., Vries T.J. On using a support vector machine in learning feed-forward control[C]. Proceedings of IEEE/ASME International Conference on Advanced Intelligent Mechatronics. 2001(1):272-277.
[63]Scholkopf B, Mika S., Burges C., et al. Input space vs. feature space in kernel-based methods[J]. IEEE Transactions on Neural Networks.1999. Vol10(5):1000-1017.
[64]Mika S., Ratsch G., Weston J., et al. Fisher discriminant analysis with kernels[C]. Neural Networks for Signal Processing Ⅸ.1999.41-48.
[65]Bach F.R. Jordan M.I. Kernel Independent Component Analysis[J]. Machine Learning Research.2002. Vol3:1-48.
[66]Ben-Hur A., Horn D., Siegelmann H.T., et al. Support vector clustering [J]. Machine Learning Research.2001. Vol2:125-137.
[67]冯汉中,陈永义.处理非线性分类和回归问题的一种新方法-支持向量机方法在天气预报中的应用[J].应用气象学报.2004.Vol15(3)：345-365.
[68]车怀敏,冯汉中.支持向量机方法在德阳降水分类预报中的应用试验.四川气象.2004,24(2)：13-15,29.
[69]马婕,樊玮,袁红玉.基于数据场的SVM技术在雷暴预报中的应用[J].计算机工程.2009.Vo135(19)：263-265.
[70]徐元顺,刘志勇,李晓兰等.支持向量机在水文气象中的应用[J].湖北水力发电.2008.Vol5：44-46,54.
[71]黄奕铭.支持向量机技术在雷雨天气预报中的应用[J].广东气象.2006.28(1)：22-24
[72]熊秋芬,曾晓青.SVM方法在降水预报中的应用及改进[J].气象.2008.Vol34(12)：90-95.
[73]罗泽举,宋丽红,薛宇峰等.一类基于SVM/RBF的气象模型预测系统.计算机工程.2006.Vol32(21)：31-32,93.
[74]王小萍,谭季青,吴书成等.基于支持向量机的预报技巧的预报模型研究[J].科技通报.2006.Vol32(6)：747-752.
[75]杨淑群,芮景析,冯汉中.支持向量机(SVM)方法在降水分类预测中的应用[J].西南农业大学学报(自然科学版).2006.Vol28(2)：252-257.
[76]Witten L.H., Frank E. Data mining practical machine learning tools and techniques(Second Edition)[M]. Morgan Kaufmann.2004.
[77]Mitchell T.M.,曾华军等译.机器学习[M].机械工业出版社.2003.
[78]Bartlett P. The sample complexity of pattern classification with neural networks:The size of the weights is more important than the size of the networks[J]. IEEE Trans on Information Theory.1998. Vol2.
[79]Burges C., Schoblkopf B. Improving the accuracy and speed of support vector machines. Advances in Neural Information Processing Systems[C]. MIT Press.1997.
[80]边肇祺,张学工等.模式识别(第2版)[M].清华大学出版社.2000.
[81]Hastie T., Tibshirani R. Classification by pairwise coupling[C]. Advances in Neural Information Processing Systems. M IT Press.1998.
[82]Campbell W.M. A SVM/HMM system for speaker recognition[C].2003 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP'03),2003:Vol2:6-10.
[83]Osuna E., Girosi F. Reducing run-time complexity in support vector machines. Advances in Kernel Methods-Support vector Learning[C]. M IT Perss.1999.271-283.
[84]Suykens J., Vandewalle J. Least squares support vector machine classifiers[J]. Neural Processing Letters.1999. Vol9:293-300.
[85]Tran Q.A., Li X., Duan H.X. Efficient performance Estimate for one-class Support Vector Machine[J]. Pattern Recognition Letters.2005. Vol26(8):1174-1182.
[86]Tax D.M., Duin R.P. Support vector domain description[J]. Pattern Recognition Letters.1999. Vol20:1191-1199.
[87]Scholkopf B., Williamson R., Smola A., et al. Support vector method for novelty description[C]. Advance in neural information processing systems. M IT press.2000. 12,582-588.
[88]Lin C.F., Wang S.D. Fuzzy support vector machines[J]. IEEE Trans on Neural Net works. 2002. Vol 13(2):464-471.
[89]李建民,张钱,林福宗.支持向量机的训练算法[J].清华人学学报.2003.Vol43(1)：120-124.
[90]Bennet K., Campbell C. Support vector machines:hype or hallelujah[J]. S1GKDD Explorations.2000. Vol2(2):1-13.
[91]Keerithi S., Shevade S., Bhattcharyya C., et al. A fast iterative nearest point algorithm for support vector machine classifier design[J]. IE EE Transactions on Neural Network.2000. Vol11(1):124-136.
[92]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.Vol24(2)：113-118.
[93]Osuna E., Freund R., Girosi F. An improved training algorithm for support vector machines[C]. Proceedings of the1997 IEEE Workshop on Neural Networks for Signal Processing.1997.276-285.
[94]Laskov P. An improved decomposition algorithm for regression support vector machines[C]. Advances in Neural Information Processing Systems. MIT P ress.2000.484-490.
[95]Laskov P. Feasible direction decomposition algorithms for training support vector machines[J]. Machine Learning.2002. Vol46(1):315-349.
[96]Platt J., Fast training of support vector machines using sequential minimal optimization[C]. Advances in Kernel Methods-Support Vector Learning. M IT Press.1999.185-208.
[97]Platt J. Using analytic QP and sparseness to speed training of support vector machines[C]. Advances in Neural Information Processing Systems. M IT P ress.1999.557-563.
[98]Francis E.H. A modified Chi2 algorithm for discretization[J]. IEEE Trans. on knowledge and data engineering.2002. Vol14(3):666-670.
[99]Keerithi S., Shevade S., Bhattacharyya C., et al. Improvements to Platt's SMO algorithm for SVM classifier design[J]. Neural Computation.2001. Vol13(3):637-649.
[100]Joachims T. Making large-scale support vector machine learning practical[C]. Advances in Kernel Methods-Support Vector Learning. M IT Press.1999.169-184.
[101]Perez Cruz F., Alarcon Diana P., Navia Vazquez A., et al. Fast training of support vector classifiers[C]. Advances in Neural Information Processing Systems. M IT P ress.2001. 734-740.
[102]Keerithi S., Gilbert E. Convergence of a generalized SMO algorithm for SVM classifier design[J]. Machine Learning.2002. Vol46 (1):351-360.
[103]Collobert R., Bengio S. SVMTorch:a support vector machine for large-scale regression and classification problems[J]. Machine Learning Research.2001. Vol1:143-160.
[104]Weston J. Extensions to t he Support Vector Method. PHD Thesis. University of London. 1999.
[105]Hsu C.W., Lin C.J. A comparison of methods for multiclass support vector machines[J]. IEEE Trans. On Neural Networks.2002. Vol13(2):415-425
[106]KerBel U. Pairwise classification and support vector machines[C]. Advances in Kernel Methods-Support Vector Leanring. MIT P ress.1999:255-268.
[107]Platt J., Cristianini N.,Taylor J. Large Margin DAGs for Multiclass Classification[C]. Advance in Neural information processing systems. M IT Press.2000:547-553.
[108]Drummond C., Holte R. Exploiting the Cost in Sensitivity of Decision Tree Splitting Criteria[C]. Proceedings of the 17th International Conference on Machine Learning.2000. 239-246.
[109]Elkan C. The Foundation of Cost-Sensitive Learning[C]. Proceedings of the 17th International Joint Conference on Artificial Intelligence.2001.239-246.
[110]肖健华,吴今培.样本数目不对称时的SVM模型[J].计算机科学.2003.Vol30(2)：165-167.
[111]Margineantu D. Class Probability Estimation and Cost-Sensitive Classification Decisions[C]. Proceedings of the 13th European Conference on Machine Learning.2002.270-281.
[112]Zadrozny B., Langford J., Abe N. Cost-Sensitive Learning by Cost-Proportionate Example Weighting[C]. Proceedings of the 3rd IEEE International Conference on Data Mining.2003.
[113]Fan W., Stolfo S., Zhang J.et al. Adacost:Misclassification Cost-Sensitive Boosting[C]. Proceedings of the 16th International Conference on Machine Learning.1999.97-105.
[114]Weiss G.M., Provost F. Learning When Training Data Are Costly:The Effect of Class Distribution on Tree Induction [J]. Artificial Intelligence Research.2003. Vol19:315-354.
[115]Zadrozny B., Elkan C. Learning and Making Decisions When Costs and Probabilities Are Both Unknown[C]. Proceedings of the 7th International Conference on Knowledge Discovery and Data Mining.2001.204-213.
[116]Weiss G.M., Hirsh H. A Quantitative Study of Small Disjuncts[C]. Proceedings of the 17th National Conference on Artificial Intelligence. AAI Press.2000.665-670.
[117]Ali K., Pazzani M. HYDRA-MM:Learning Multiple Descriptions to Improve Classification Accuracy. Artificial Intelligence Tools.1995. Vol4.
[118]Holte R.C., Acker L.E., Porter B.W. Concept Learning and the Problem of Small Disjuncts[C]. Proceedings of the 11th International Joint Conference on Artificial Intelligence. 1989.813-818.
[119]Van Den Bosch A., Weijters T., Van Den Herik H.J., et al. When Small Disjuncts Abound. Try Lazy Learnning:A Case Study[C]. Proceedings of the 7th Belgian-Dutch Conference on Machine Learning.1997.109-118.
[120]Liu B., Hsu W., Ma Y. Mining Association Rules with Multiple Minimum Supports[C]. Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.1999.337-341.
[121]Friedman J. H., Kohavi R., Yun Y. Lazy Decision Trees[C]. Proceedings of the 13th National Conference on Artificial Intelligence.1996.717-724.
[122]Weiss G.M. Timeweaver:A Genetic Algorithm for Identifying Predictive Patterns in Sequences of Events[C]. Proceedings of the Genetic and Evolutionary Computation Conference.1999.718-725.
[123]Estabrooks A., Japkowicz N. A Mixture-of-Experts Framework for Learning from Unbalanced Data Sets[C]. Proceedings of the 2001 Intelligent Data Analysis Conference.2001. 34-43.
[124]Joshi M.V., Agarwal R.C., Kumar V. Mining Needles in a Haystack:Classifying Rare Classes Via Two-Phase Rule Induction[C]. SIGMOD01 Conference on Management of Data.2001.91-102.
[125]Joshi M.V., Agarwal R.C., Kumar V. Predicting Rare Classes:Can Boosting Make Any Weak Learner Strong? [C]. Proceedings of the 8th International Conference on Knowledge Discovery and Data Mining.2002.297-306.
[126]Bradley A. The Use of the Area under the ROC Curve in the Evaluation of Machine Learning Algorithms[J]. Pattern Recognition.1997. Vol30(7):1145-1159.
[127]Provost F., Fawcett T. Robust Classincation for Imprecise Environments[J]. Machine Learning.2001. Vol42:203-231.
[128]Japkowicz N., Stephen S. The Class Imbalance problem:A Systematic Study[J]. Intelligent Data Analysis.2002. Vol6(5):429-450.
[129]Carvalho D.R., Freitas A. A Genetic Algorithm for Discovering Small-disjunct Rules in Data Mining[J]. Applied Soft Computing.2002. Vol2(2):75-88.
[130]Van Rijsbergen C.J. Information Retrieval(2nd Edition). Butterworths.1979.
[131]Quinlan J.R. Improved Estimates for the Accuracy of Small Disjuncts[J]. Machine Learning. 1991. Vol:93-98.
[132]Riddle P., Segal R., Etzioni O. Representation Design and Brute-force Induction in a Boeing Manufacturing Design[J]. Applied Artificial Intelligence.1994. Vol8:125-147.
[133]Agarwal R., Imielinski T., Swami A. Mining Association Rules Between Sets of Items in Large Databases[C]. Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data.1993.207-217.
[134]Ting K.M. The Problem of Small Disjuncts:Its Remedy in Decision Trees[C]. Proceeding of the 10th Canadian Conference on Artificial Intelligence.1994.91-97.
[135]Japkowicz N., Myers C., Gluck M.A. A Novelty Detection Approach to Classification[C]. Proceedings of the 14th Joint Conference on Artificial Intelligence.1995.518-523.
[136]Raskutti B., Kowalczyk A. Extreme Re-Balancing for SVMs:A Case Study In Workshop on Learning from Imbalanced Data Sets[C]. International Conference on Machine Learning. 2003.
[137]Kubat M., Matwin S. Addressing the Curse of Imbalanced Training Sets:One-sided Selection[C]. Proceedings of the 14th International Conference on Machine Learning.1997. 179-186.
[138]Cohen W.W. Fast Effective Rule Induction[C]. Proceedings of the 12th International Conference on Machine Learning.1995.115-123.
[139]Kubat M., Holte R.C., Matwin S. Machine Learning for the Detection of oil Spills in Satellite Radar Images[J]. Machine Learning.1998. Vol30(2):195-215.
[140]Breiman L., Friedman J., Oishen R., et al. Classification and Regression Trees[M]. Chapman and Hall/CRC Press.1984.
[141]Drummond C, Holte R.C. C4.5, Class Imbalance, and Cost Sensitivity:Why Under-Sampling Beats Over-Sampling[C]. Workshop on Learning from Imbalanced Data Sets, International Conference on Machine Learning.2003.
[142]Chawla N.V., Bowyer K.W., Hall L.O. et al. SMOTE:Synthetic Minority Over-sampling Technique[J]. Artificial Intelligence Research.2002. Vol 16:321-357.
[143]Chan P.K., Stolfo S.J. Toward Scalable Learning with Non-uniform Class and Cost Distributions:A Case Study in Credit Card Fraud Detection[C] Proceedings of the 4th International Conference on Knowledge Discovery and Data Mining.2001.164-168.
[144]Weiss G.M., Provost F. Learning When Training Data Are Costly:The Effect of Class Distribution on Tree Induction[J]. Artificial Intelligence Research.2003. Vol19:315-354.
[145]Cardie C. Improving Minority Class Prediction Using Case Specific Feature Weights[C]. Proceedings of the 14th International Conference on Machine Learning.1997.57-65.
[146]Schapire R.E. A Brief Introduction to Boosting[C]. Proceedings of the 16th International joint Conference on Artificial Intelligence.1999.1401-1406.
[147]Chawla N.V., Lazarevic A., Hall L.O., et al. SMOTEBoost:Improving Prediction of the Minority Class in Boosting[C]. Proceedings of Principles of Knowledge Discovery in Databases.2003.
[148]Fayyad U., Haussler D., Stolorz P. KDD for Science Data Analysis:Issues and Examples[C]. Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining(KDD96). AAAI Press.1996.
[149]Fawcett T., Provost F.J. Activity Monitoring:Noticing Interesting Charges in Behavior[C]. Proceedings of the 5th International Conference on Knowledge Discovery and Data Mining. KDD-99.1999.
[150]Fawcett T., Provost F.J. Adaptive Fraud Detection[J]. Data Mining and Knowledge Discovery.1997. Vol3.
[151]Turney P.D. Cost Sensitive Classification:Empirical Evaluation of a Hybrid Genetic Decision Tree Induction Algorithm[J]. Artificial Intelligence Research.1995. Vol2:369-409.
[152]Tan M. Cost Sensitive Learning of Classification Knowledge and Its Applications in Robotics[J]. Machine Learning.1993. Vol13:7-33.
[153]NUNez M. Economic Induction:A Case Study[C]. Proceedings of the 3rd European Working Session on Learning. EWSL-88.1988.139-145.
[154]NUNez M. The Use of Background Knowledge in Decision Tree Induction. Machine Learning.1991. Vol6:231-250.
[155]Krogh A., Vedelsby J. Neural Network Ensembles, Cross Validation and Active Learning[C]. Neural Information Processing Systems. MIT Press.1995.231-238.
[156]Hasenjager M., Ritter H. Active Learning with Local Models[J]. Neural Processing Letters. 1998. Vol7(2):107-117.
[157]Cohn D.A., Ghahramani Z., Jordan M.I. Active Learning With Statistical Models[J]. Artificial Intelligence Research.1996. Vol4:129-145.
[158]Domingos P. Knowledge Discovery Via Multiple Models[J]. Intelligent Data Analysis.1998. Vol2:187-202.
[159]Chow C.K. On Optimum Error and Reject Tradeoff[J]. IEEE Trans. on Information Theory. 1970. Vol 16:41-46.
[160]Ge M., Du R., Zhang G. Fault Diagnosis Using Support Vector Machine with an Application in Sheet Metal Stamping Operations[J]. Mechanical Systems and Signal Processing.2004. Vol18:143-159.
[161]Chang R.F., Wu W.J. Support Vector Machines for Diagnosis of Breast Tumors on US Images[J]. Academic Radiology.2003. Vol10(2):189-197.
[162]Freund Y., Schapire R.E. A Decision-Theoretic Generalization of on-Line Learning and an Application to Boosting[J]. Computer and System Sciences.1997. Vol55(1):119-139.
[163]吴高巍,陶卿,王珏.基于后验概率的SVM[J].计算机研究与发展.2005.Vol42(2)：196-202.
[164]萧嵘,王继成,孙正兴等.一种SVM增量学习算法a-ISVM.软件学报.2001.Voll2(12)：1818-1824.
[165]Ratsaby J. Incremental learning with sample queries[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence.1998. Vol20(8):883-888.
[166]Yamauchi K., Yamaguchi N., Ishii N. Incremental learning methods with retrieving of interfered patterns[J]. IEEE Transactions on Neural Networks.1999. Vol10(11):1351-1365.
[167]Fung G., Mangasaran O.L. Incremental support vector machine classification[M]. Madison. 2001.
[168]Syed N.A., Liu H., Sung K.K. Incremental learning with support vector machines[C]. Proceedings of the International joint Conference on Artificial Intelligence(IJCAI-99).1999.
[169]Ralaivola L., Dalche-Buc F. Incremental support vector machine learning:a local approach[C]. Proceedings of the International Conference on Artificial Neural Networks(ICANN).2001.
[170]Tveit A., Lie M., Engum H. Incremental and decremental proximal support vector classification using decay coefficients[C]. Proceedings of the 5th International Conference on Data Warehousing and Knowledge Discovery.2003.
[171]Cauwenberghs G., Poggio T. Incremental and decremental support vector machine learning[J]. Advance Neural Information Processing.2001 Vol13.
[172]陶亮.基于活跃集迭代法的支持向量机快速增量算法[J].系统仿真学报.2006.Voll8(11)：3305-3312.
[173]Mnagasarian O.L., Musicant D. Lagrangian support vector machines[J]. Machine Learning Research.2001.161-177.
[174]Mnagasarian O.L., Solodov M.V. Nonlinear complementarity as constrained and constrained minimization[J]. Mathematical Programming, Series B.1993. Vol62:277-297.
[175]Mnagasarian O.L., Musicnat D.R. Successive overrelaxation for support vector machines[J]. IEEE Transactions on Neural Newtokrs.1999. Vol 10:1032-1037.
[176]Mnagasarian O.L., Musicnat D.R. Active support vector machines classification[C]. Advances in Neural Information Processing Systems(NIPS 2000).2000.
[177]Lee Y.J., Mnagasarian O.L. SSVM:A smooth support vector machines for classification[J]. Computational Optimization and Applications.2000. Vol20(1):5-22.
[178]袁亚湘,孙文瑜.最优化理论与方法[M].科学出版社.1997.
[179]Ma J.S. Accurate online support vector regression[J]. Neural Computation.2003. Vol15(11):2683-2703.
[180]Carozza M., Ranpone S. Towards an incremental SVM for regression[C]. Proceedings of the International Joint Conference on Neural Networks. IJCNN2000.
[181]Shao X.J., He G.P. Lagrange support vector regression[J]. Intelligent Information Management Systems and Technologies.2005. Vol1(3):434-440.
[182]张家诚等.中国气候[M].上海科学技术出版社.1986.251-260.
[183]Keogh J., Pazzani M.I. An enhanced representation of time series which allows fast and accurate classification, clustering and relevance feedback[C]. Proceedings of the 4th International Conference of Knowledge Discovery and Data Mining.1998.239-243.
[184]汤庸.时态数据库导论[M].北京大学出版社.2004.
[185]Takeuchi J., Yamanishi K. A unifying framework for detecting outliers and change points from time series[J]. IEEE Transactions on Knowledge and Data Engineering.2006. Vol18(4):482-492.
[186]Keogh E., Lonardi S., Chiu W. Finding surprising patterns in a time series database in linear time and space[C].8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.2002.550-556.
[187]黄嘉佑.气象统计分析与预报方法.气象出版社.1990.
[188]Muller K.R. Predicting Time Series with Support Vector Machines[C]. Proceeding of ICANN'97.1997:999-1004.
[189]Mattera D., Haykin S. Support Vector Machine for dynamic Reconstruction of a Chaotic System. Advances in kernel methods-Support Vector Learning. MIT press.1999:211-242.
[190]孙德山,吴今培,侯振挺等.基于SVR的混沌时间序列预测[J].计算机工程与应用.2004.Vol2：54-56.
[191]Francis EH T., Cao L.J. Modified Support Vector Machines in Financial Time Series Forecasting[J]. Neurocomputing.2002. Vol(48):847-861.
[192]中国气象局监测网络司.《地面气象电码手册》.气象出版社,1999.
[193]李东晖,杜树新,吴铁军.基于壳向量的线性支持向量机快速增量学习算法[J].浙江大学学报：工学版,2006,40(2)：203-207
[194]林升梁,刘志.基于RBF核函数的支持向量机参数选择[J].浙江工业大学学报.2007.Vol35(2)：163-167.
[195]Kuoping W., Shengde W. Choosing the kernel parameters for support vector machines by the inter-cluster distance in the feature space[J]. Pattern Recognition.2009. Vol42(5):710-717.
[196]王睿.关于支持向量机参数选择方法分析[J].重庆师范大学学报(自然科学版).2007.Vol24(2)：36-38.
[197]李京华,张聪颖,倪宁.基于参数优化的支持向量机战场多目标声识别[J].探测与控制学报.2010.Vol32(1)：1-5.
[198]郑恩辉.基于支持向量机的代价敏感数据挖掘研究与应用[D].浙江大学博士学位论文.2006.
[199]段华.支持向量机的增量学习算法研究[D].上海交通大学博士学位论文.2008.
[200]李丽娟.最小二乘支持向量机建模及预测控制算法研究[D].浙江大学博十学位论文.2008.
[201]董晓莉.时间序列数据挖掘相似性度量和周期模式挖掘研究[D].天津大学博士学位论文.2007.
[202]Cohen W.W. Fast Effective Rule Induction[C]. Proceedings of the 12th International Conference on Machine Learning.1995.115-123.
[203]李智才,马文瑞,李素敏等.支持向量机在短期气候预测中的应用[J].气象.2006.Vol32(5)：57-61.
[204]熊秋芬,顾永刚,王丽.支持向量机分类方法在天空云量预报中的应用[J].气象.2007.Vol33(5)：20-26.
[205]王珏,周志华,周傲英.机器学习及其应用[M].清华大学出版社.2006.
[2]Bouttier F., Coutier, P.. Data assimilation and Concepts and methods[P]. ECMWF train notes. 1999.
[3]王跃山.客观分析和四维同化一站在新世纪的回望(Ⅱ)客观分析的主要方法(2)[J].气象科技.2001,Vol(3)：25-28.
[4]张卫民.气象资料变分同化的研究与并行计算实现[D].国防科学技术大学博士学位论文.2005
[5]Han J., Kamber M. Data Mining:Concepts & Techniques [M]. Morgan Kaufmann Publishers. 2001:30-33.
[6]朱明.数据挖掘[M].中国科技大学出版社.2002：35-36.
[7]Michael Steinbach, Pang-Ning Tan, Vipin Kumar, et al. Discovery of climate indices using clustering[C]. Proceedings of the ninth ACM SIGK.DD international conference on Knowledge discovery and data mining.2003.
[8]杨炳儒.数据挖掘领域的两大猜想.通讯和计算机.2007.Vol 4(4)：1-4,8.
[9]陈旭辉.沙尘暴资料的数据挖掘算法分析及系统实现[D].兰州大学硕士学位论文.2008
[10]马开玉,张耀辉,陈星.现代应用统计学[M].气象出版社.2003.
[11]王得民,施能.气象统计预报[M].气象出版社.1990.
[12]陈宝学,俞经善,关宏伟.数据挖掘技术应用于天气预报的可行性研究[J].应用科技.2004.Vol3：20-23.
[13]Xiang Li, Rahul Ramachandran, Sara Graves, et al. Automated detection of frontal systems from numerical model-generated data[C]. Proceeding of the eleventh ACM SIGKDD international conference On Knowledge discovery in data mining.2005:765-771.
[14]Yang Yubin, Lin Hui, Guo Zhongyang, et al. A meteorological conceptual modeling approach based on spatial data mining and knowledge discovery[C]. Proceedings of the 18th international conference on Innovations in Applied Artificial Intelligence.2005:342-346.
[15]Rie Honda, Shuai Wang, Tokio Kikuchi, et al. Mining of Moving Objects from Time-Series Images and its Application to Satellite Weather Imagery[J]. Journal of Intelligent Information Systems.2002. Vol19(1):79-93.
[16]Sherri K. Harms, Jitender S. Deogun. Sequential Association Rule Mining with Time Lags[J]. Journal of Intelligent Information Systems.2004. Vol22(1):7-22.
[17]Auroop R. Ganguly. A Hybrid Approach to Improving Rainfall Forecasts[J]. Computing in Science and Engineering.2002. Vol4(4):14-21.
[18]Jiang Zhao, Chang-Tien Lu, Yufeng Kou. Detecting region outliers in meteorological data[C]. Proceedings of the 11th ACM international symposium on Advances in geographic information systems.2003.
[19]Kenneth A. Hawick, P. D. Coddington, H.A. James. Distributed frameworks and parallel algorithms for processing large-scale geographic data[J]. Parallel Computing.2003. Vol29(10):1297-1333.
[20]Wallace J. M., Smith C., Bvetherton C. S. Singular value decomposition of winter time sea surface temperature and 500-mb height anomalies[J]. Journal of climate.1992. Vol5(6):561-576.
[21]Silverman D., Dracup J. Artificial network and long-range precipitation in California[J]. Journal of Applied Meteorology.2000. Vol31(1):57-66.
[22]文俊浩,向宏,李立新等.基于邻接关系的空间聚类算法研究[J].计算机工程与应用.2003.Vol39(34)：184-186.
[23]Sherri K. Harms, Jitender S. Deogun. Sequential Association Rule Mining with Time Lags[J]. Journal of Intelligent Information Systems.2004. Vol22(1):7-22.
[24]李娜娜,宋洁,顾军华等.灾害天气关联模式挖掘技术研究[J].河北工业大学学报.2005.Vol34(2)：68-73.
[25]McCann D.W. A neural network short-term forecast of significant thunderstorms[J]. Weather and Forecasting.1992. Vol7:525-534.
[26]Baik Longjin, Hwang Hongsub. Tropical cyclone intensity prediction using regression method and neural network[J]. Journal of the Meteorology Society of Japan.1998. Vol76(5):711-717.
[27]金龙,吴建生.基于遗传算法的神经网络短期预报预测模型[J].灾害学.2004.Vol8(1)：15-16.
[28]曹晓钟,刘还珠.神经网络在气象应用中的并行实现[J].计算机工程与应用.2003.Vol36：207-209.
[29]金龙,况雪源,黄海洪.人工神经网络预报模型的过拟合研究[J].气象学报.2004.Vol62(1)：62-70.
[30]张迎春,肖冬荣,赵远东.基于时间序列神经网络的气象预测研究[J].武汉大学学报(交通科学与工程版).2003.Vol27(2)：237-240.
[31]Rie Honda, Shuai Wang, Tokio Kikuchi,et al. Mining of Moving Objects from Time-Series Images and its Application to Satellite Weather Imagery[J]. Journal of Intelligent Information Systems.2002. Vol19(1):79-93.
[32]石扬,张燕平,赵妹等.基于商空间的气象时间序列数据挖掘研究[J].计算机工程与应用.2007.Vo143(1)：201-203.
[33]李集明,沈文海,王国复.气象信息共享平台及其关键技术研究[J].应用气象学报.2006.Vol17(5)：60-62.
[34]谭晓光.数据仓库技术在天气预报决策中的应用探讨[J].应用气象学报.2006.Vol 17(3)：325-332.
[35]刘伟,宋倩,陈佳.气象OLAP系统数据显示的解决方案[J].计算机工程与设计.2006.Vo127(3)：482-484.
[36]Biing T. Guana, Hsin-Wu Hsua, Tsong-Huei Weyb, et al. Modeling monthly mean temperatures for the mountain regions of Taiwan by generalized additive models[J]. Agricultural and Forest Meteorology.2009. Vol149(2):281-290
[37]Vapnik V.N. Statistical Learning Theory[M]. J.Wiley.1998.
[38]Boser B., Guyon I., Vapnik V.N. A training algorithm for optimal margin classifiers[C]. Fifth Annual Workshop on Computational Learning Theory. ACM Press.1992
[39]Scholkopf B., Burges C., Vapnik V.N. Extracting support data for a given task[C]. Proceedings of First International Conference on Knowledge Discovery and Data Mining, AAAI Press.1995:262-267.
[40]Guyon I., Boser B., Vapnik V.N. Automatic capacity turning of very large VC-dimension classifiers. Advances in Neural Information Processing Systerns[J]. Morgan Kaufmann.1993 Vol5:147-155.
[41]Cortes C., Vapnik V.N. Support vector networks[J]. Machine Learning.1995 Vol20:273-297.
[42]Vapnik V.N., Golowieh S., Smola A. Support vector method for function approximation, Regression Estimation, and Signal Processing. Advances in Neural Information Processing Systems[M]. Cambridge, MA.1997:281-287.
[43]邓乃扬,田英杰.数据挖掘中的最优化方法一支持向量机[M].科学出版社.2004.
[44]Cristianini N., Shawe-Yaylor J.,李国正等译.支持向量机导论[M].电子工业出版社.2004.
[45]Scholkopf B., Burges C., Vapnik V.N. Incorporating invariance in support vector learning machines. Artificial Neural Networks-ICANN'96, Lecture Notes in Computer Science, 1996(1112):47-52.
[46]Scholkopf B., Bartlett P., Smola A. et al. Support vector regression with automatic accuracy control. Proceeding of the 8th International conference on Artificial Neural Networks. 1998:111-116.
[47]Drucker H., Burges C., Kaufman L., et al. Support vector regression machines, Advances in Neural Information Processing Systems[M]. MITPress.1997:155-161.
[48]Pontil M., Rifkin R., Evgeniou T. From regression to classification in support machines. Proceedings ESANN.1999:225-230.
[49]Chang C., Lin C. Training v-support vector regression:theory and algorithms [J]. Neural Computation.2002. Vol14(8):1959-1977.
[50]Smola A.J., Scholkopf B., Muller K.R. The connection between regularization operators and support vector Kernels[J]. Neural Networks.1998. Vol11:637-649.
[51]Muller K.R., Smola A.J. Predicting time series with support vector machines[C]. Proceeding of the International Conference on Artificial Neural Networks.1997.999-1004.
[52]Mukherjee S., Osuna E., Girosi F. Nonlinear prediction of chaotic time series using a support vector machine[C]. Neural Networks for Signal Processing Ⅶ-Proceeding of the 1997 IEEE Workshop.1997.511-520.
[53]付岩,王耀威,王伟强等.SVM用于基于内容的自然图像分类和检索[J].计算机学报.2003.Vol26(10)：1261-1265.
[54]张宪,李晓娟.支持向量机在显微图像分类中的应用研究.计算机应用.2008.Vol28(3)：790-794.
[55]王新明,梁维泰,周方等.基于支持向量机和Getis因子的高分辨率遥感图像分类[J].地理与地理信息科学.2008.Vol24(4)：16-19,24.
[56]居红云,张俊本,李朝峰等.基于K-means与SVM结合的遥感图像全自动分类方法[J].计算机应用研究.2007.Vol24(11)：318-320.
[57]Brown M., Gunn S.R. Empirical data modelling algorithms:additive spline models
[58]and support vector machines[C]. Proc of UKACC International Conference on Control. 1998(1):709-714.
[59]王定成,方廷健,高理富等.支持向量机回归在线建模及应用[J].控制与决策.2003.Vol18(1)：89-91.
[60]张浩然,韩正之,李昌刚.基于支持向量机的非线性系统辨识[J].系统仿真学报.2003.Vol15(1)：119-121.
[61]Suykens A.K., Vandewalle J., Demoor B. Optimal control by least squares support vector machines[J]. Neural Networks.2001. Vol14:105-113.
[62]Kruif B.J., Vries T.J. On using a support vector machine in learning feed-forward control[C]. Proceedings of IEEE/ASME International Conference on Advanced Intelligent Mechatronics. 2001(1):272-277.
[63]Scholkopf B, Mika S., Burges C., et al. Input space vs. feature space in kernel-based methods[J]. IEEE Transactions on Neural Networks.1999. Vol10(5):1000-1017.
[64]Mika S., Ratsch G., Weston J., et al. Fisher discriminant analysis with kernels[C]. Neural Networks for Signal Processing Ⅸ.1999.41-48.
[65]Bach F.R. Jordan M.I. Kernel Independent Component Analysis[J]. Machine Learning Research.2002. Vol3:1-48.
[66]Ben-Hur A., Horn D., Siegelmann H.T., et al. Support vector clustering [J]. Machine Learning Research.2001. Vol2:125-137.
[67]冯汉中,陈永义.处理非线性分类和回归问题的一种新方法-支持向量机方法在天气预报中的应用[J].应用气象学报.2004.Vol15(3)：345-365.
[68]车怀敏,冯汉中.支持向量机方法在德阳降水分类预报中的应用试验.四川气象.2004,24(2)：13-15,29.
[69]马婕,樊玮,袁红玉.基于数据场的SVM技术在雷暴预报中的应用[J].计算机工程.2009.Vo135(19)：263-265.
[70]徐元顺,刘志勇,李晓兰等.支持向量机在水文气象中的应用[J].湖北水力发电.2008.Vol5：44-46,54.
[71]黄奕铭.支持向量机技术在雷雨天气预报中的应用[J].广东气象.2006.28(1)：22-24
[72]熊秋芬,曾晓青.SVM方法在降水预报中的应用及改进[J].气象.2008.Vol34(12)：90-95.
[73]罗泽举,宋丽红,薛宇峰等.一类基于SVM/RBF的气象模型预测系统.计算机工程.2006.Vol32(21)：31-32,93.
[74]王小萍,谭季青,吴书成等.基于支持向量机的预报技巧的预报模型研究[J].科技通报.2006.Vol32(6)：747-752.
[75]杨淑群,芮景析,冯汉中.支持向量机(SVM)方法在降水分类预测中的应用[J].西南农业大学学报(自然科学版).2006.Vol28(2)：252-257.
[76]Witten L.H., Frank E. Data mining practical machine learning tools and techniques(Second Edition)[M]. Morgan Kaufmann.2004.
[77]Mitchell T.M.,曾华军等译.机器学习[M].机械工业出版社.2003.
[78]Bartlett P. The sample complexity of pattern classification with neural networks:The size of the weights is more important than the size of the networks[J]. IEEE Trans on Information Theory.1998. Vol2.
[79]Burges C., Schoblkopf B. Improving the accuracy and speed of support vector machines. Advances in Neural Information Processing Systems[C]. MIT Press.1997.
[80]边肇祺,张学工等.模式识别(第2版)[M].清华大学出版社.2000.
[81]Hastie T., Tibshirani R. Classification by pairwise coupling[C]. Advances in Neural Information Processing Systems. M IT Press.1998.
[82]Campbell W.M. A SVM/HMM system for speaker recognition[C].2003 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP'03),2003:Vol2:6-10.
[83]Osuna E., Girosi F. Reducing run-time complexity in support vector machines. Advances in Kernel Methods-Support vector Learning[C]. M IT Perss.1999.271-283.
[84]Suykens J., Vandewalle J. Least squares support vector machine classifiers[J]. Neural Processing Letters.1999. Vol9:293-300.
[85]Tran Q.A., Li X., Duan H.X. Efficient performance Estimate for one-class Support Vector Machine[J]. Pattern Recognition Letters.2005. Vol26(8):1174-1182.
[86]Tax D.M., Duin R.P. Support vector domain description[J]. Pattern Recognition Letters.1999. Vol20:1191-1199.
[87]Scholkopf B., Williamson R., Smola A., et al. Support vector method for novelty description[C]. Advance in neural information processing systems. M IT press.2000. 12,582-588.
[88]Lin C.F., Wang S.D. Fuzzy support vector machines[J]. IEEE Trans on Neural Net works. 2002. Vol 13(2):464-471.
[89]李建民,张钱,林福宗.支持向量机的训练算法[J].清华人学学报.2003.Vol43(1)：120-124.
[90]Bennet K., Campbell C. Support vector machines:hype or hallelujah[J]. S1GKDD Explorations.2000. Vol2(2):1-13.
[91]Keerithi S., Shevade S., Bhattcharyya C., et al. A fast iterative nearest point algorithm for support vector machine classifier design[J]. IE EE Transactions on Neural Network.2000. Vol11(1):124-136.
[92]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.Vol24(2)：113-118.
[93]Osuna E., Freund R., Girosi F. An improved training algorithm for support vector machines[C]. Proceedings of the1997 IEEE Workshop on Neural Networks for Signal Processing.1997.276-285.
[94]Laskov P. An improved decomposition algorithm for regression support vector machines[C]. Advances in Neural Information Processing Systems. MIT P ress.2000.484-490.
[95]Laskov P. Feasible direction decomposition algorithms for training support vector machines[J]. Machine Learning.2002. Vol46(1):315-349.
[96]Platt J., Fast training of support vector machines using sequential minimal optimization[C]. Advances in Kernel Methods-Support Vector Learning. M IT Press.1999.185-208.
[97]Platt J. Using analytic QP and sparseness to speed training of support vector machines[C]. Advances in Neural Information Processing Systems. M IT P ress.1999.557-563.
[98]Francis E.H. A modified Chi2 algorithm for discretization[J]. IEEE Trans. on knowledge and data engineering.2002. Vol14(3):666-670.
[99]Keerithi S., Shevade S., Bhattacharyya C., et al. Improvements to Platt's SMO algorithm for SVM classifier design[J]. Neural Computation.2001. Vol13(3):637-649.
[100]Joachims T. Making large-scale support vector machine learning practical[C]. Advances in Kernel Methods-Support Vector Learning. M IT Press.1999.169-184.
[101]Perez Cruz F., Alarcon Diana P., Navia Vazquez A., et al. Fast training of support vector classifiers[C]. Advances in Neural Information Processing Systems. M IT P ress.2001. 734-740.
[102]Keerithi S., Gilbert E. Convergence of a generalized SMO algorithm for SVM classifier design[J]. Machine Learning.2002. Vol46 (1):351-360.
[103]Collobert R., Bengio S. SVMTorch:a support vector machine for large-scale regression and classification problems[J]. Machine Learning Research.2001. Vol1:143-160.
[104]Weston J. Extensions to t he Support Vector Method. PHD Thesis. University of London. 1999.
[105]Hsu C.W., Lin C.J. A comparison of methods for multiclass support vector machines[J]. IEEE Trans. On Neural Networks.2002. Vol13(2):415-425
[106]KerBel U. Pairwise classification and support vector machines[C]. Advances in Kernel Methods-Support Vector Leanring. MIT P ress.1999:255-268.
[107]Platt J., Cristianini N.,Taylor J. Large Margin DAGs for Multiclass Classification[C]. Advance in Neural information processing systems. M IT Press.2000:547-553.
[108]Drummond C., Holte R. Exploiting the Cost in Sensitivity of Decision Tree Splitting Criteria[C]. Proceedings of the 17th International Conference on Machine Learning.2000. 239-246.
[109]Elkan C. The Foundation of Cost-Sensitive Learning[C]. Proceedings of the 17th International Joint Conference on Artificial Intelligence.2001.239-246.
[110]肖健华,吴今培.样本数目不对称时的SVM模型[J].计算机科学.2003.Vol30(2)：165-167.
[111]Margineantu D. Class Probability Estimation and Cost-Sensitive Classification Decisions[C]. Proceedings of the 13th European Conference on Machine Learning.2002.270-281.
[112]Zadrozny B., Langford J., Abe N. Cost-Sensitive Learning by Cost-Proportionate Example Weighting[C]. Proceedings of the 3rd IEEE International Conference on Data Mining.2003.
[113]Fan W., Stolfo S., Zhang J.et al. Adacost:Misclassification Cost-Sensitive Boosting[C]. Proceedings of the 16th International Conference on Machine Learning.1999.97-105.
[114]Weiss G.M., Provost F. Learning When Training Data Are Costly:The Effect of Class Distribution on Tree Induction [J]. Artificial Intelligence Research.2003. Vol19:315-354.
[115]Zadrozny B., Elkan C. Learning and Making Decisions When Costs and Probabilities Are Both Unknown[C]. Proceedings of the 7th International Conference on Knowledge Discovery and Data Mining.2001.204-213.
[116]Weiss G.M., Hirsh H. A Quantitative Study of Small Disjuncts[C]. Proceedings of the 17th National Conference on Artificial Intelligence. AAI Press.2000.665-670.
[117]Ali K., Pazzani M. HYDRA-MM:Learning Multiple Descriptions to Improve Classification Accuracy. Artificial Intelligence Tools.1995. Vol4.
[118]Holte R.C., Acker L.E., Porter B.W. Concept Learning and the Problem of Small Disjuncts[C]. Proceedings of the 11th International Joint Conference on Artificial Intelligence. 1989.813-818.
[119]Van Den Bosch A., Weijters T., Van Den Herik H.J., et al. When Small Disjuncts Abound. Try Lazy Learnning:A Case Study[C]. Proceedings of the 7th Belgian-Dutch Conference on Machine Learning.1997.109-118.
[120]Liu B., Hsu W., Ma Y. Mining Association Rules with Multiple Minimum Supports[C]. Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.1999.337-341.
[121]Friedman J. H., Kohavi R., Yun Y. Lazy Decision Trees[C]. Proceedings of the 13th National Conference on Artificial Intelligence.1996.717-724.
[122]Weiss G.M. Timeweaver:A Genetic Algorithm for Identifying Predictive Patterns in Sequences of Events[C]. Proceedings of the Genetic and Evolutionary Computation Conference.1999.718-725.
[123]Estabrooks A., Japkowicz N. A Mixture-of-Experts Framework for Learning from Unbalanced Data Sets[C]. Proceedings of the 2001 Intelligent Data Analysis Conference.2001. 34-43.
[124]Joshi M.V., Agarwal R.C., Kumar V. Mining Needles in a Haystack:Classifying Rare Classes Via Two-Phase Rule Induction[C]. SIGMOD01 Conference on Management of Data.2001.91-102.
[125]Joshi M.V., Agarwal R.C., Kumar V. Predicting Rare Classes:Can Boosting Make Any Weak Learner Strong? [C]. Proceedings of the 8th International Conference on Knowledge Discovery and Data Mining.2002.297-306.
[126]Bradley A. The Use of the Area under the ROC Curve in the Evaluation of Machine Learning Algorithms[J]. Pattern Recognition.1997. Vol30(7):1145-1159.
[127]Provost F., Fawcett T. Robust Classincation for Imprecise Environments[J]. Machine Learning.2001. Vol42:203-231.
[128]Japkowicz N., Stephen S. The Class Imbalance problem:A Systematic Study[J]. Intelligent Data Analysis.2002. Vol6(5):429-450.
[129]Carvalho D.R., Freitas A. A Genetic Algorithm for Discovering Small-disjunct Rules in Data Mining[J]. Applied Soft Computing.2002. Vol2(2):75-88.
[130]Van Rijsbergen C.J. Information Retrieval(2nd Edition). Butterworths.1979.
[131]Quinlan J.R. Improved Estimates for the Accuracy of Small Disjuncts[J]. Machine Learning. 1991. Vol:93-98.
[132]Riddle P., Segal R., Etzioni O. Representation Design and Brute-force Induction in a Boeing Manufacturing Design[J]. Applied Artificial Intelligence.1994. Vol8:125-147.
[133]Agarwal R., Imielinski T., Swami A. Mining Association Rules Between Sets of Items in Large Databases[C]. Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data.1993.207-217.
[134]Ting K.M. The Problem of Small Disjuncts:Its Remedy in Decision Trees[C]. Proceeding of the 10th Canadian Conference on Artificial Intelligence.1994.91-97.
[135]Japkowicz N., Myers C., Gluck M.A. A Novelty Detection Approach to Classification[C]. Proceedings of the 14th Joint Conference on Artificial Intelligence.1995.518-523.
[136]Raskutti B., Kowalczyk A. Extreme Re-Balancing for SVMs:A Case Study In Workshop on Learning from Imbalanced Data Sets[C]. International Conference on Machine Learning. 2003.
[137]Kubat M., Matwin S. Addressing the Curse of Imbalanced Training Sets:One-sided Selection[C]. Proceedings of the 14th International Conference on Machine Learning.1997. 179-186.
[138]Cohen W.W. Fast Effective Rule Induction[C]. Proceedings of the 12th International Conference on Machine Learning.1995.115-123.
[139]Kubat M., Holte R.C., Matwin S. Machine Learning for the Detection of oil Spills in Satellite Radar Images[J]. Machine Learning.1998. Vol30(2):195-215.
[140]Breiman L., Friedman J., Oishen R., et al. Classification and Regression Trees[M]. Chapman and Hall/CRC Press.1984.
[141]Drummond C, Holte R.C. C4.5, Class Imbalance, and Cost Sensitivity:Why Under-Sampling Beats Over-Sampling[C]. Workshop on Learning from Imbalanced Data Sets, International Conference on Machine Learning.2003.
[142]Chawla N.V., Bowyer K.W., Hall L.O. et al. SMOTE:Synthetic Minority Over-sampling Technique[J]. Artificial Intelligence Research.2002. Vol 16:321-357.
[143]Chan P.K., Stolfo S.J. Toward Scalable Learning with Non-uniform Class and Cost Distributions:A Case Study in Credit Card Fraud Detection[C] Proceedings of the 4th International Conference on Knowledge Discovery and Data Mining.2001.164-168.
[144]Weiss G.M., Provost F. Learning When Training Data Are Costly:The Effect of Class Distribution on Tree Induction[J]. Artificial Intelligence Research.2003. Vol19:315-354.
[145]Cardie C. Improving Minority Class Prediction Using Case Specific Feature Weights[C]. Proceedings of the 14th International Conference on Machine Learning.1997.57-65.
[146]Schapire R.E. A Brief Introduction to Boosting[C]. Proceedings of the 16th International joint Conference on Artificial Intelligence.1999.1401-1406.
[147]Chawla N.V., Lazarevic A., Hall L.O., et al. SMOTEBoost:Improving Prediction of the Minority Class in Boosting[C]. Proceedings of Principles of Knowledge Discovery in Databases.2003.
[148]Fayyad U., Haussler D., Stolorz P. KDD for Science Data Analysis:Issues and Examples[C]. Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining(KDD96). AAAI Press.1996.
[149]Fawcett T., Provost F.J. Activity Monitoring:Noticing Interesting Charges in Behavior[C]. Proceedings of the 5th International Conference on Knowledge Discovery and Data Mining. KDD-99.1999.
[150]Fawcett T., Provost F.J. Adaptive Fraud Detection[J]. Data Mining and Knowledge Discovery.1997. Vol3.
[151]Turney P.D. Cost Sensitive Classification:Empirical Evaluation of a Hybrid Genetic Decision Tree Induction Algorithm[J]. Artificial Intelligence Research.1995. Vol2:369-409.
[152]Tan M. Cost Sensitive Learning of Classification Knowledge and Its Applications in Robotics[J]. Machine Learning.1993. Vol13:7-33.
[153]NUNez M. Economic Induction:A Case Study[C]. Proceedings of the 3rd European Working Session on Learning. EWSL-88.1988.139-145.
[154]NUNez M. The Use of Background Knowledge in Decision Tree Induction. Machine Learning.1991. Vol6:231-250.
[155]Krogh A., Vedelsby J. Neural Network Ensembles, Cross Validation and Active Learning[C]. Neural Information Processing Systems. MIT Press.1995.231-238.
[156]Hasenjager M., Ritter H. Active Learning with Local Models[J]. Neural Processing Letters. 1998. Vol7(2):107-117.
[157]Cohn D.A., Ghahramani Z., Jordan M.I. Active Learning With Statistical Models[J]. Artificial Intelligence Research.1996. Vol4:129-145.
[158]Domingos P. Knowledge Discovery Via Multiple Models[J]. Intelligent Data Analysis.1998. Vol2:187-202.
[159]Chow C.K. On Optimum Error and Reject Tradeoff[J]. IEEE Trans. on Information Theory. 1970. Vol 16:41-46.
[160]Ge M., Du R., Zhang G. Fault Diagnosis Using Support Vector Machine with an Application in Sheet Metal Stamping Operations[J]. Mechanical Systems and Signal Processing.2004. Vol18:143-159.
[161]Chang R.F., Wu W.J. Support Vector Machines for Diagnosis of Breast Tumors on US Images[J]. Academic Radiology.2003. Vol10(2):189-197.
[162]Freund Y., Schapire R.E. A Decision-Theoretic Generalization of on-Line Learning and an Application to Boosting[J]. Computer and System Sciences.1997. Vol55(1):119-139.
[163]吴高巍,陶卿,王珏.基于后验概率的SVM[J].计算机研究与发展.2005.Vol42(2)：196-202.
[164]萧嵘,王继成,孙正兴等.一种SVM增量学习算法a-ISVM.软件学报.2001.Voll2(12)：1818-1824.
[165]Ratsaby J. Incremental learning with sample queries[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence.1998. Vol20(8):883-888.
[166]Yamauchi K., Yamaguchi N., Ishii N. Incremental learning methods with retrieving of interfered patterns[J]. IEEE Transactions on Neural Networks.1999. Vol10(11):1351-1365.
[167]Fung G., Mangasaran O.L. Incremental support vector machine classification[M]. Madison. 2001.
[168]Syed N.A., Liu H., Sung K.K. Incremental learning with support vector machines[C]. Proceedings of the International joint Conference on Artificial Intelligence(IJCAI-99).1999.
[169]Ralaivola L., Dalche-Buc F. Incremental support vector machine learning:a local approach[C]. Proceedings of the International Conference on Artificial Neural Networks(ICANN).2001.
[170]Tveit A., Lie M., Engum H. Incremental and decremental proximal support vector classification using decay coefficients[C]. Proceedings of the 5th International Conference on Data Warehousing and Knowledge Discovery.2003.
[171]Cauwenberghs G., Poggio T. Incremental and decremental support vector machine learning[J]. Advance Neural Information Processing.2001 Vol13.
[172]陶亮.基于活跃集迭代法的支持向量机快速增量算法[J].系统仿真学报.2006.Voll8(11)：3305-3312.
[173]Mnagasarian O.L., Musicant D. Lagrangian support vector machines[J]. Machine Learning Research.2001.161-177.
[174]Mnagasarian O.L., Solodov M.V. Nonlinear complementarity as constrained and constrained minimization[J]. Mathematical Programming, Series B.1993. Vol62:277-297.
[175]Mnagasarian O.L., Musicnat D.R. Successive overrelaxation for support vector machines[J]. IEEE Transactions on Neural Newtokrs.1999. Vol 10:1032-1037.
[176]Mnagasarian O.L., Musicnat D.R. Active support vector machines classification[C]. Advances in Neural Information Processing Systems(NIPS 2000).2000.
[177]Lee Y.J., Mnagasarian O.L. SSVM:A smooth support vector machines for classification[J]. Computational Optimization and Applications.2000. Vol20(1):5-22.
[178]袁亚湘,孙文瑜.最优化理论与方法[M].科学出版社.1997.
[179]Ma J.S. Accurate online support vector regression[J]. Neural Computation.2003. Vol15(11):2683-2703.
[180]Carozza M., Ranpone S. Towards an incremental SVM for regression[C]. Proceedings of the International Joint Conference on Neural Networks. IJCNN2000.
[181]Shao X.J., He G.P. Lagrange support vector regression[J]. Intelligent Information Management Systems and Technologies.2005. Vol1(3):434-440.
[182]张家诚等.中国气候[M].上海科学技术出版社.1986.251-260.
[183]Keogh J., Pazzani M.I. An enhanced representation of time series which allows fast and accurate classification, clustering and relevance feedback[C]. Proceedings of the 4th International Conference of Knowledge Discovery and Data Mining.1998.239-243.
[184]汤庸.时态数据库导论[M].北京大学出版社.2004.
[185]Takeuchi J., Yamanishi K. A unifying framework for detecting outliers and change points from time series[J]. IEEE Transactions on Knowledge and Data Engineering.2006. Vol18(4):482-492.
[186]Keogh E., Lonardi S., Chiu W. Finding surprising patterns in a time series database in linear time and space[C].8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.2002.550-556.
[187]黄嘉佑.气象统计分析与预报方法.气象出版社.1990.
[188]Muller K.R. Predicting Time Series with Support Vector Machines[C]. Proceeding of ICANN'97.1997:999-1004.
[189]Mattera D., Haykin S. Support Vector Machine for dynamic Reconstruction of a Chaotic System. Advances in kernel methods-Support Vector Learning. MIT press.1999:211-242.
[190]孙德山,吴今培,侯振挺等.基于SVR的混沌时间序列预测[J].计算机工程与应用.2004.Vol2：54-56.
[191]Francis EH T., Cao L.J. Modified Support Vector Machines in Financial Time Series Forecasting[J]. Neurocomputing.2002. Vol(48):847-861.
[192]中国气象局监测网络司.《地面气象电码手册》.气象出版社,1999.
[193]李东晖,杜树新,吴铁军.基于壳向量的线性支持向量机快速增量学习算法[J].浙江大学学报：工学版,2006,40(2)：203-207
[194]林升梁,刘志.基于RBF核函数的支持向量机参数选择[J].浙江工业大学学报.2007.Vol35(2)：163-167.
[195]Kuoping W., Shengde W. Choosing the kernel parameters for support vector machines by the inter-cluster distance in the feature space[J]. Pattern Recognition.2009. Vol42(5):710-717.
[196]王睿.关于支持向量机参数选择方法分析[J].重庆师范大学学报(自然科学版).2007.Vol24(2)：36-38.
[197]李京华,张聪颖,倪宁.基于参数优化的支持向量机战场多目标声识别[J].探测与控制学报.2010.Vol32(1)：1-5.
[198]郑恩辉.基于支持向量机的代价敏感数据挖掘研究与应用[D].浙江大学博士学位论文.2006.
[199]段华.支持向量机的增量学习算法研究[D].上海交通大学博士学位论文.2008.
[200]李丽娟.最小二乘支持向量机建模及预测控制算法研究[D].浙江大学博十学位论文.2008.
[201]董晓莉.时间序列数据挖掘相似性度量和周期模式挖掘研究[D].天津大学博士学位论文.2007.
[202]Cohen W.W. Fast Effective Rule Induction[C]. Proceedings of the 12th International Conference on Machine Learning.1995.115-123.
[203]李智才,马文瑞,李素敏等.支持向量机在短期气候预测中的应用[J].气象.2006.Vol32(5)：57-61.
[204]熊秋芬,顾永刚,王丽.支持向量机分类方法在天空云量预报中的应用[J].气象.2007.Vol33(5)：20-26.
[205]王珏,周志华,周傲英.机器学习及其应用[M].清华大学出版社.2006.