Reducing SVR Support Vectors by Using Backward Deletion

Authors:
Masayuki Karasuyama;Ichiro Takeuchi;Ryohei Nakano
Affiliations:
Nagoya Institute of Technology, Nagoya, Japan 466-8555;Nagoya Institute of Technology, Nagoya, Japan 466-8555;Nagoya Institute of Technology, Nagoya, Japan 466-8555
Venue:
KES '08 Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part III
Year:
2008

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Abstract

Support Vector Regression (SVR) is one of the most famous sparse kernel machines which inherits many advantages of Support Vector Machines (SVM). However, since the number of support vectors grows rapidly with the increase of training samples, sparseness of the SVR is sometimes insufficient. In this paper, we propose two methods which reduce the SVR support vectors using backward deletion. Experiments show our method can dramatically reduce the number of support vectors without sacrificing the generalization performance.