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Indexing Support Vector Machines for Efficient top-$k$ Classification

Authors:
Giuseppe Amato
Paolo Bolettieri
Fabrizio Falchi
Fausto Rabitti
Pasquale Savino

Keywords: Image Classification; Support Vector Machines; Similarity Searching

Abstract:
This paper proposes an approach to efficiently execute approximate top-k classification (that is, identifying the best k elements of a class) using Support Vector Machines, in web-scale datasets, without significant loss of effectiveness. The novelty of the proposed approach, with respect to other approaches in literature, is that it allows speeding-up several classifiers, each one defined with different kernels and kernel parameters, by using one single index.

Pages: 56 to 61

Copyright: Copyright (c) IARIA, 2011

Publication date: April 17, 2011

Published in: conference

ISSN: 2308-4448

ISBN: 978-1-61208-129-8

Location: Budapest, Hungary

Dates: from April 17, 2011 to April 22, 2011