PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Analysis of SVM regression bounds for variable ranking
Alain Rakotomamonjy
(2005) Technical Report. PSI, Rouen.

Abstract

This paper addresses the problem of variable ranking for Support Vector Regression. The relevance criteria that we proposed are based on leave-one-out bounds and some variants and for these criteria we have compared di®erent search space algorithms : recursive feature elimination and scaling factor optimization based on gradient descent. All these algorithms have been compared on some toy problems and realworld QSAR datasets. Results showed that the span estimate criterion optimized through gradient descent yields improved error rate with fewer variables and that an interesting alternative criterion when the number of variables is very large can be a criterion based only on the lagrangian multipliers of the Support Vector Regression problem.

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EPrint Type:Monograph (Technical Report)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:1391
Deposited By:Alain Rakotomamonjy
Deposited On:28 November 2005