PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Relevance regression learning with support vector machines
Bruno Apolloni, Dario Malchiodi and Lorenzo Valerio
Nonlinear Analysis Volume 73, pp. 2855-2867, 2010. ISSN 0362-546X

Abstract

We propose a variant of two SVM regression algorithms expressly tailored in order to exploit additional information summarizing the relevance of each data item, as a measure of its relative importance w.r.t. the remaining examples. These variants, enclosing the original formulations when all data items have the same relevance, are preliminary tested on synthetic and real-world data sets. The obtained results outperform standard SVM approaches to regression if evaluated in light of the above mentioned additional information about data quality.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:7006
Deposited By:Dario Malchiodi
Deposited On:23 September 2010