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.
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
Support vector machines
Similarity searching
H.3.1 Content Analysis and Indexing
H.3.3 Information Search and Retrieval

Icona documento 1) Download Document PDF

Icona documento Open access Icona documento Restricted Icona documento Private


Per ulteriori informazioni, contattare: Librarian http://puma.isti.cnr.it

Valid HTML 4.0 Transitional