PUMA
Istituto di Scienza e Tecnologie dell'Informazione     
Amato G., Bolettieri P., Falchi F., Rabitti F., Savino P. Indexing support vector machines for efficient top-k classification. In: MMEDIA 2011 - Third International Conferences on Advances in Multimedia (Budapest, Hungary, 17-22 Aprile 2011). Proceedings, pp. 56 - 61. XPS (Xpert Publishing Services), 2011.
 
 
Abstract
(English)
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.
URL: http://www.thinkmind.org/index.php?view=article&articleid=mmedia_2011_3_10_40012
Subject Machine learning
Classification
Support vector machines
Similarity searching
H.3.1 Content Analysis and Indexing
H.3.3 Information Search and Retrieval


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