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Similarity Learning for Nearest Neighbor Classification

Abstract : In this paper, we propose an algorithm for learning a general class of similarity measures for kNN classification. This class encompasses, among others, the standard cosine measure, as well as the Dice and Jaccard coefficients. The algorithm we propose is an extension of the voted perceptron algorithm and allows one to learn different types of similarity functions (either based on diagonal, symmetric or asymmetric similarity matrices). The results we obtained show that learning similarity measures yields significant improvements on several collections, for two prediction rules: the standard kNN rule, which was our primary goal, and a symmetric version of it.
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Contributor : Marie-Christine Fauvet Connect in order to contact the contributor
Submitted on : Friday, February 28, 2014 - 4:02:45 PM
Last modification on : Wednesday, July 6, 2022 - 4:12:15 AM


  • HAL Id : hal-00953867, version 1


Ali Mustafa Qamar, Eric Gaussier, Jean-Pierre Chevallet, Joo-Hwee Lim. Similarity Learning for Nearest Neighbor Classification. 8th IEEE International Conference on Data Mining (ICDM) 2008, 2008, Pisa, Italy. pp.983-988. ⟨hal-00953867⟩



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