PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Double Chunking for Solving SVMs for Very Large Datasets
Fernando Perez-Cruz, Anibal Figueiras and Antonio Artes
In: Learning'04, 22-24 Oct 2004, Alicante, Spain.

Abstract

In this paper we present a novel approach to solve the SVM for very large training sets. We propose to substitute the shrinking technique for a double chunk, so we are able to exploit the cache memory more efficiently and obtain the solution in considerably less runtime complexity.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:1184
Deposited By:Fernando Perez-Cruz
Deposited On:19 November 2005