Online learning algorithm of kernel-based ternary classifiers using support vectors

被引：0
作者：
Kovalchuk A.V. [1 ]
Bellyustin N.S. [2 ]
机构：
[1] Institute of Applied Physics of the Russian Academy of Sciences, Nizhny Novgorod
[2] Radiophysical Scientific-Research Institute, Nizhny Novgorod
来源：
Optical Memory and Neural Networks | 2013年 / 22卷 / 3期
关键词：
online learning; support vector machine; ternary classifier;
D O I：
10.3103/S1060992X13030089
中图分类号：
学科分类号：
摘要：
An algorithm OnSVM of the kernel-based classification is proposed which solution is very close to -SVM an efficient modification of support vectors machine. The algorithm is faster than batch implementations of -SVM and has a smaller resulting number of support vectors. The approach developed maximizes a margin between a pair of hyperplanes in feature space and can be used in online setup. A ternary classifier of 2-class problem with an "unknown" decision is constructed using these hyperplanes. © 2013 Allerton Press, Inc.
引用
收藏
页码：193 / 205
页数：12
相关论文
共 15 条
[1]  
Cristianini, N., Shawe, J., Taylor, J., (2000) An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods
[2]  
Cauwenberghs, G., Poggio, T., (2000) Incremental and Decremental Support Vector Machine Learning, pp. 409-415
[3]  
Bordes, A., Ertekin, S., Weston, J., Bottou, L., Fast kernel classifiers with online and active learning (2005) Journal of Machine Learning Research, 6, pp. 1579-1619
[4]  
Roobaert, D., (2002) Direct SVM: A Simple Support Vector Machine Perceptron, pp. 147-156
[5]  
Orabona, F., Castellini, C., Caputo, B., Jie, L., Sandini, G., On-line independent support vector machines (2010) Pattern Recognition, 43, pp. 1402-1412
[6]  
Schoelkopf, B., Smola, A., (1998) New Support Vector Algorithms
[7]  
Vapnik, V.N., Algorithms and Programms of Dependences Reconstruction Ed., Moscow: Fizmatlit [In Russian]
[8]  
Vapnik, V., (1982) Estimation of Dependences Based on Empirical Data, , Berlin: Springer-Verlag
[9]  
Vetrov, D.P., Kropotov, D.A., (2006) Algorithms of Models Choice and Classification Task Collective Decision Construction, Which Based on Stability Principles, , Moscow: KomKniga
[10]  
Platt, J., Fast Training of Support Vector Machines Using Sequential Minimal Optimization (1999) Advances in Kernel Methods - Support Vector Learning, pp. 185-208. , B. Scholkopf, C. J. C. Burges, and A. J. Smola (Eds.)
12