正在加载图片...
474 智能系统学报 第3卷 化学习算法[J].哈尔滨工程大学学报,2007,28(2): on-line algorithms[J].IEEE Trans on Information Theory, 183-188. 2008,54(1):386-390. ZHU Qidan,ZHANG Zhi,XING Zhuoyi.Improved SMO [49]STEINWART I.Sparseness of support vector machines learing method of support vector machine [J].Joural of [J].Journal of Machine Learn Research,2003,4(3): Harbin Engineering University,2007,28(2):183-188. 1071-1105. [38]张浩然,汪晓东.回归最小二乘支持向量机的增量和 [50]刘向东,陈兆乾。一种快速支持向量机分类算法的研 在线式学习算法[J].计算机学报,2006,29(3):400- 究[J刀.计算机研究与发展,2004,41(8):1327-1332. 406. LIU Xiangdong,CHEN Zhaoqian.A fast classification al- ZHANG Haoran,WANG Xiaodong.Incremental and on- gorithm of support vector machines[J].Journal of Comput- line leaming algorithm for regression least squares support er Research and Development,2004,41 (8):1327- vector machine[J].Chinese Journal of Computers,2006, 1332. 29(3):400-406. [51]WU M,SCHOLKOPF B,BAKIR G.A direct method for [39]杨静,张健沛,刘大昕.基于多支持向量机分类器的 building sparse kemnel learning algorithms[J].Joural of 增量学习算法研究[J].哈尔滨工程大学学报,2006,27 Machine Learning Research,2006,7(4):603-624. (1):103-106. [52]NGUYEN D,HO T.An efficient method for simplifying YANG Jing,ZHANG Jianpei,LIU Daxin.Research on in- support vector machines[C]//International Conference on cremental leaming algorithm with multiple support vector Machine Learning.Bonn,Germany,2005:617-624. machine classifiers [J].Journal of Harbin Engineering U- [53 ]NGUYEN D.HO T.A bottom-up method for simplifying niversity,2006,27(1):103-106. support vector solutions [J].IEEE Trans on Neural Net- [40]汪辉.增量型支持向量机回归训练算法及在控制中 woks,2006,17(3):792-796. 的应用[D].杭州:浙江大学,2006. [54]KEERTHI SS,CHAPELLE O,DECOSTE D.Building WANG Hui.Incremental support vector machine regression support vector machines with reduced classifier complexity training algorithm and its application in control [D].Hang- [J].Journal of Machine Learning Research,2006,7 zhu:zhejiang University,2006. (7):1493-1515. [41]MA J H,THEILER J,PERKINS S.Accurate on-line sup- [55]LI Qing,JIAO Licheng,HAO Yingjuan.Adaptive simpli- port vector regression[J].Neural Computation,2003,15 fication of solution for support vector machine[J].Patter (11):2683-2703. Recognition,2007,40(3):972-980. [42]VOJISLAV K,MICHAEL V,HUANG Teming.On the e- [56 ]SUMEET A,SARADHI VV,HARISH K.Kemel-based quality of kemel AdaTron and sequential minimal optimiza- online machine learning and support vector reduction[J]. tion in classification and regression tasks and alike algo- Neurocomputing,2008,71(9):1230-1237. rithms for kemnel machines[C]//European Symposium on [57]CRAMMER K,KANDOLA J,SINGER Y.Online classifi- Artificial Neural Networks.Bruges,Belgium:D-side Publi- cation on a budget[C]//Advances in Neural Information cations,2003:215-222. Processing Systems.Whistler,Canada,2003:225-232. [43]YAAKOV E,SHIE M,RON M.Sparse online greedy sup- [58 ]WESTON J,BORDES A,BOTTOU L.Online and off- port vector regression [C]//Machine learning:ECMI line)on an even tighter budget[C]//Proceedings of the 2002.Berlin:Springer-Verlag,2002:84-96. Tenth Interational Workshop on Artificial Intelligence and [44]KIVINEN J,SMOLA A J,WILLIAMSON R C.Online Statistics.Barbados,2005:413-420. leaming with kemels[J].IEEE Trans on Signal Process- [59]DEKEL O,SHWARTZ SS,SINGER Y.The forgetron:A ing,2004,52(8):2165-2176. kemnel-based perceptron on a fixed budget[Cl//Advances [45]SMALE S,YAO Y.Online learning algorithms[J].Foun- in Neural Information Processing Systems.Vancouver,Can- dations of Computational Mathematics,2006,6(3):145- ada,2005:259-266. 170. [60]OFER D,YORAM S.Support vector machines on a budget [46]YING Yiming,ZHOU Dingxuan,Online regularized classi- [C]//Advances in Neural Information Processing Sys- fication algorithms[J].IEEE Trans on Information Theo- tems.Whistler,Canada,2006:345-352. y,2006,52(11):47754788. [61]JOACHIMS T.Training linear SVMs in linear time[C]// [47]BIANCHI N C,CONCONI A,GENTILE C.On the gener- International Conference on Knowledge Discovery and Data alization ability of on-line leaming algorithms[J].IEEE Mining(KDD).New York,USA,2006:217-226. Trans on Information Theory,2004,50(9):2050-2057. [62]SHWARTZ S S,SINGER Y,SREBRO N.Pegasos:Pri- [48]BIANCHI N C,GENTILE C.Improved risk tail bounds for mal estimated sub-gradient solver for SVM[C]//Proceed-
<<向上翻页向下翻页>>
©2008-现在 cucdc.com 高等教育资讯网 版权所有