Regularized classifiers for information retrieval

Authors:
Abderrezak Brahmi;Ahmed Ech-Cherif
Affiliations:
LAMOSI Laboratory – Department of Computer Science, University of Sciences and Technology-USTO Mohamed Boudiaf, Oran El M'Naouer, Algeria;LAMOSI Laboratory – Department of Computer Science, University of Sciences and Technology-USTO Mohamed Boudiaf, Oran El M'Naouer, Algeria
Venue:
AI'05 Proceedings of the 18th Canadian Society conference on Advances in Artificial Intelligence
Year:
2005

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Abstract

We study a class of binary regularized least-squares classifiers (RLSC) for information retrieval tasks whose training involve the solution of a unique linear system of equations Any implementation of RLSC algorithms face two major difficulties: the large size and the density of the Gram matrix In this paper, we present a numerical investigation of an implementation based on the preconditioned conjugate gradient and introduce a novel reduced RBF kernel which is shown to improve the sparseness of the system.