Chapter

Artificial Neural Networks — ICANN 2002

Volume 2415 of the series Lecture Notes in Computer Science pp 687-693

Date:

Selection of Meta-parameters for Support Vector Regression

  • Vladimir CherkasskyAffiliated withDepartment of Electrical and Computer Engineering, University of Minnesota
  • , Yunqian MaAffiliated withDepartment of Electrical and Computer Engineering, University of Minnesota

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Abstract

We propose practical recommendations for selecting metaparameters for SVM regression (that is, ε -insensitive zone and regularization parameter C). The proposed methodology advocates analytic parameter selection directly from the training data, rather than resampling approaches commonly used in SVM applications. Good generalization performance of the proposed parameter selection is demonstrated empirically using several low-dimensional and high-dimensional regression problems. In addition, we compare generalization performance of SVM regression (with proposed choiceε) with robust regression using ‘least-modulus’ loss function (ε=0). These comparisons indicate superior generalization performance of SVM regression.