NDLI: Prototype learning with margin-based conditional log-likelihood loss

Content Provider	IEEE Xplore Digital Library
Author	Xiaobo Jin Cheng-Lin Liu Xinwen Hou
Copyright Year	2008
Description	Author affiliation: Nat. Lab. of Pattern Recognition, Chinese Acad. of Sci., Beijing, China (Xiaobo Jin; Cheng-Lin Liu; Xinwen Hou)
Abstract	The classification performance of nearest prototype classifiers largely relies on the prototype learning algorithms, such as the learning vector quantization (LVQ) and the minimum classification error (MCE). This paper proposes a new prototype learning algorithm based on the minimization of a conditional log-likelihood loss (CLL), called log-likelihood of margin (LOGM). A regularization term is added to avoid over-fitting in training. The CLL loss in LOGM is a convex function of margin, and so, gives better convergence than the MCE algorithm. Our empirical study on a large suite of benchmark datasets demonstrates that the proposed algorithm yields higher accuracies than the MCE, the generalized LVQ (GLVQ), and the soft nearest prototype classifier (SNPC).
Starting Page	1
Ending Page	4
File Size	553489
Page Count	4
File Format	PDF
ISBN	9781424421749
ISSN	10514651
DOI	10.1109/ICPR.2008.4760953
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-12-08
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Prototypes Convergence Performance loss Vector quantization Minimization methods Nearest neighbor searches Training data Testing Laboratories Pattern recognition
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition

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