Kernel machine based feature extraction algorithms for regression problems

Csaba Szepesvári, András Kocsor, K. Kovács

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper we consider two novel kernel machine based feature extraction algorithms in a regression settings. The first method is derived based on the principles underlying the recently introduced Maximum Margin Discimination Analysis (MMDA) algorithm. However, here it is shown that the orthogonalization principle employed by the original MMDA algorithm can be motivated using the well-known ambiguity decomposition, thus providing a firm ground for the good performance of the algorithm. The second algorithm combines kernel machines with average derivative estimation and is derived from the assumption that the true regressor function depends only on a subspace of the original input space. The proposed algorithms are evaluated in preliminary experiments conducted with artificial and real datasets.

Original languageEnglish
Title of host publicationECAI 2004 - 16th European Conference on Artificial Intelligence, including Prestigious Applications of Intelligent Systems, PAIS 2004 - Proceedings
PublisherIOS Press
Pages1093-1094
Number of pages2
ISBN (Electronic)9781586034528
Publication statusPublished - Jan 1 2004
Event16th European Conference on Artificial Intelligence, ECAI 2004 - Valencia, Spain
Duration: Aug 22 2004Aug 27 2004

Publication series

NameFrontiers in Artificial Intelligence and Applications
Volume110
ISSN (Print)0922-6389

Other

Other16th European Conference on Artificial Intelligence, ECAI 2004
CountrySpain
CityValencia
Period8/22/048/27/04

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ASJC Scopus subject areas

  • Artificial Intelligence

Cite this

Szepesvári, C., Kocsor, A., & Kovács, K. (2004). Kernel machine based feature extraction algorithms for regression problems. In ECAI 2004 - 16th European Conference on Artificial Intelligence, including Prestigious Applications of Intelligent Systems, PAIS 2004 - Proceedings (pp. 1093-1094). (Frontiers in Artificial Intelligence and Applications; Vol. 110). IOS Press.