PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

An experimental analysis of the impact of accuracy degradation in SVM classification
Dario Malchiodi
International Journal of Computational Intelligence Studies Volume 1, Number 2, pp. 163-190, 2009.

Abstract

The aim of this paper is to analyse the phenomenon of accuracy degradation in the samples given as input to SVM classification algorithms. In particular, the effect of accuracy degradation on the performance of the learnt classifiers is investigated and compared, if possible, with theoretical results. The study shows how a family of SVM classification algorithms enhanced in order to deal with quality measures on the available data handles accuracy degradation better than the classical SVM approaches to classification.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:6105
Deposited By:Dario Malchiodi
Deposited On:08 March 2010