电气工程 |
|
|
|
|
一类支持向量机的快速增量学习方法 |
王洪波, 赵光宙, 齐冬莲, 卢达 |
浙江大学 电气工程学院, 浙江 杭州 310027 |
|
Fast incremental learning method for one-class support vector machine |
WANG Hong-bo, ZHAO Guang-zhou, QI Dong-lian, LU Da |
College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China |
引用本文:
王洪波, 赵光宙, 齐冬莲, 卢达. 一类支持向量机的快速增量学习方法[J]. J4, 2012, 46(7): 1327-1332.
WANG Hong-bo, ZHAO Guang-zhou, QI Dong-lian, LU Da. Fast incremental learning method for one-class support vector machine. J4, 2012, 46(7): 1327-1332.
链接本文:
http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2012.07.027
或
http://www.zjujournals.com/eng/CN/Y2012/V46/I7/1327
|
[1] VAPNIK V N. The nature of statistical learning theory [M]. New York: Springer, 1995.
[2] 潘志松, 陈斌, 缪志敏, 等. OneClass分类器研究 [J]. 电子学报, 2009, 37(11): 2496-2503.
PAN Zhisong, CHEN Bin, MIAO Zhimin, et al. Overview of study on oneclass classifiers [J]. ACTA Electronica Sinica, 2009, 37(11): 2496-2503.
[3] 王钰, 周志华, 周傲英. 机器学习及其应用 [M]. 北京: 清华大学出版社, 2006: 32-56.
[4] SCHLKOPF B, WILLIANMSON R, SMOLA A, et al. Support vector method for novelty detection [J]. Advances in Neural Information Processing Systems, 2000, 12(3): 582-588.
[5] TAX D M J, DUIN R P W. Support vector data description [J]. Machine Learning, 2004, 54(1): 45-66.
[6] 徐磊, 赵光宙, 顾弘. 基于作用集的一类支持向量机递推式训练算法 [J]. 浙江大学学报: 工学版, 2009, 43(1): 42-46.
XU Lei, ZHAO Guangzhou, GU Hong. Recursive training algorithm for one class support vector machine based on active set method [J]. Journal of Zhejiang University: Engineering Science, 2009, 43(1): 42-46.
[7] MUOZMAR J, BOVOLO F, GMEZCHOVA L, et al. Semisupervised oneclass support vector machines for classification of remote sensing data [J]. IEEE Transactions on Geoscience and Remote Sensing, 2010, 48(8): 3188-3197.
[8] SYED N A, LIU H, SUNG K K. Incremental learning with support vector machines [C]∥ Proceedings of the Workshop on Support Vector Machines at the International Joint Conference on Artificial Intelligence (IJCA199). Stockholm, Sweden: IEEE, 1999.
[9]CAUWENBERGHS G, POGGIO T. Incremental and decremental support vector machine learning [J]. Machine Learning, 2001, 44(13): 409-415.
[10] 孔锐, 张冰. 一种快速支持向量机增量学习算法 [J]. 控制与决策, 2005, 20(10): 1129-1132.
KONG Rui, ZHANG Bing. A fast incremental learning algorithm for support vector machine [J]. Control and Decision, 2005, 20(10): 1129-1132.
[11] TAX D M J, PAVEL L. Online SVM learning: from classification to data description and back [C]∥ Proceedings of 13th Workshop on Neural Networks for Signal Processing. Molina: IEEE, 2003: 499-508.
[12] KIM P J, CHANG H J, CHOI J Y. Fast incremental learning for oneclass support vector classifier using sample margin information [C]∥ Proceedings of 19th International Conference on Pattern Recognition (ICPR). Tampa: IEEE, 2008: 1-4.
[13] BERTSEKAS D P, HAGER W W, MANGASARIAN O L. Nonlinear programming [M]. New York: Athena Scientific Belmont, MA, 1999.
[14] SCHLKOPF B, BURGES C J C, SMOLA A J. Advances in kernel methods: support vector learning [M]. [S. l.]: MIT, 1998: 185-208.
[15] FLAKE G W, LAWRENCE S. Efficient SVM regression training with SMO [J]. Machine Learning, 2002, 46(1): 271-290.
[16] SCHLKOPF B, PLATT J C, JOHN S T, et al. Estimating the support of a highdimensional distribution [J]. Neural Computation, 2001, 13(7): 1443-1471.
[17] FRANK A, ASUNCION A. UCI machine learning repository [DB/OL]. [20100301]. http:∥archive.ics.uci.edu/ml. |
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
|
Shared |
|
|
|
|
|
Discussed |
|
|
|
|