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      Practical selection of SVM parameters and noise estimation for SVM regression.

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          Abstract

          We investigate practical selection of hyper-parameters for support vector machines (SVM) regression (that is, epsilon-insensitive zone and regularization parameter C). The proposed methodology advocates analytic parameter selection directly from the training data, rather than re-sampling approaches commonly used in SVM applications. In particular, we describe a new analytical prescription for setting the value of insensitive zone epsilon, as a function of training sample size. Good generalization performance of the proposed parameter selection is demonstrated empirically using several low- and high-dimensional regression problems. Further, we point out the importance of Vapnik's epsilon-insensitive loss for regression problems with finite samples. To this end, we compare generalization performance of SVM regression (using proposed selection of epsilon-values) with regression using 'least-modulus' loss (epsilon=0) and standard squared loss. These comparisons indicate superior generalization performance of SVM regression under sparse sample settings, for various types of additive noise.

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          Author and article information

          Journal
          Neural Netw
          Neural networks : the official journal of the International Neural Network Society
          Elsevier BV
          0893-6080
          0893-6080
          Jan 2004
          : 17
          : 1
          Affiliations
          [1 ] Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN 55455, USA. cherkass@ece.umn.edu
          Article
          S0893-6080(03)00169-2
          10.1016/S0893-6080(03)00169-2
          14690712
          69be2e33-d8eb-436b-93b5-3cedd2d7d7ed
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