Similarity Learning for Nearest Neighbor Classification
作者： Ali Mustafa Qamar , Eric Gaussier , Jean-Pierre Chevallet , Joo Hwee Lim
DOI: 10.1109/ICDM.2008.81
关键词: Class (biology) 、 k-nearest neighbors algorithm 、 Perceptron 、 Similarity (network science) 、 Pattern recognition 、 Mathematics 、 Symmetric matrix 、 Jaccard index 、 Artificial intelligence 、 Euclidean distance 、 Similarity learning
摘要: In this paper, we propose an algorithm for learning a general class of similarity measures kNN classification. This encompasses, among others, the standard cosine measure, as well Dice and Jaccard coefficients. The is extension voted perceptron allows one to learn different types functions (either based on diagonal, symmetric or asymmetric matrices). results obtained show that yields significant improvements several collections, two prediction rules: rule, which was our primary goal, version it.
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ieeecomputersociety.org参考文章(14)
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