A Method to Reduce Samples for Support Vector Machines

被引：2

作者：

Zhang, Guodong ^{[1
]}

Zhou, Ju ^{[1
]}

Guo, Wei ^{[1
]}

机构：

[1] Shenyang Aerosp Univ, Sch Comp, Shenyang 110136, Peoples R China

来源：

2014 INTERNATIONAL CONFERENCE ON VIRTUAL REALITY AND VISUALIZATION (ICVRV2014) | 2014年

基金：

中国国家自然科学基金; 美国国家科学基金会;

关键词：

sample reduction; support vector machines; boundary sample;

D O I：

10.1109/ICVRV.2014.19

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Training Support Vector Machines (SVMs) needs to solve a very large quadratic programming (QP) optimization problem. The traditional methods (e.g., Newton's method pound (c) are used to solve this problem, which could lead to train slowly and occupy much memory, especially for large training sets. These disadvantages limit the application of the SVMs. To improve the training speed of SVMs and reduce the storage requirement memory, this paper develops a new method to reduce the number of the training data by extracting the boundary samples from the original sets. The artificial sets and UCI sets are used to test the performance of our method. When the training sets are linearly separable (e.g., LS-600 and LS-1600), the compression rates can reach 93.8% and 98.7%, while the accuracy both reached 100.0%. The performance of method to the non-linear case is still well. These experiment results show that the method proposed could reduce the number of training data and guarantee the accuracy of classification.

引用

页码：248 / 253

页数：6

共 13 条

[1]

Burges C. J. C, 1998, DATA MIN KNOWL DISC, P955

[2]

Chang C.-C., 2004, Libsvm: a library for support vector machines, software

[3]

Cortes C., 1995, MACHINE LEARN, V1995, P273, DOI DOI 10.1007/BF00994018

[4]

JOACHIMS T., 2009, MACH LEARN, V27, P27

[5]

Li Q., 2005, CHINESE J COMPUTERS, P145

[6] An improved training algorithm for support vector machines [J].

Osuna, E ;

Freund, R ;

Girosi, F .

NEURAL NETWORKS FOR SIGNAL PROCESSING VII, 1997, :276-285

[7]

Platt J., 1988, KERNEL METHODS SUPPO

[8]

Platt JC, 1999, ADVANCES IN KERNEL METHODS, P185

[9]

Tsang IW, 2005, J MACH LEARN RES, V6, P363

[10]

Vapnik V., 1998, Statistical Learning Theory, P5

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