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Regularized margin-based conditional log-likelihood loss for prototype learning

文献类型：期刊论文

作者Jin, Xiao-Bo; Liu, Cheng-Lin; Hou, Xinwen
刊名PATTERN RECOGNITION
出版日期2010-07-01
卷号43期号:7页码:2428-2438
关键词Prototype learning Conditional log-likelihood loss Log-likelihood of margin (LOGM) Regularization Distance metric learning
英文摘要The classification performance of nearest prototype classifiers largely relies on the prototype learning algorithm. The minimum classification error (MCE) method and the soft nearest prototype classifier (SNPC) method are two important algorithms using misclassification loss. This paper proposes a new prototype learning algorithm based on the conditional log-likelihood loss (CLL), which is based on the discriminative model called log-likelihood of margin (LOGM). A regularization term is added to avoid over-fitting in training as well as to maximize the hypothesis margin. The CLL in the LOGM algorithm is a convex function of margin, and so, shows better convergence than the MCE. In addition, we show the effects of distance metric learning with both prototype-dependent weighting and prototype-independent weighting. Our empirical study on the benchmark datasets demonstrates that the LOGM algorithm yields higher classification accuracies than the MCE, generalized learning vector quantization (GLVQ), soft nearest prototype classifier (SNPC) and the robust soft learning vector quantization (RSLVQ), and moreover, the LOGM with prototype-dependent weighting achieves comparable accuracies to the support vector machine (SVM) classifier. Crown Copyright (C) 2010 Published by Elsevier Ltd. All rights reserved.
WOS标题词Science & Technology ; Technology
类目[WOS]Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
研究领域[WOS]Computer Science ; Engineering
关键词[WOS]VECTOR QUANTIZATION ; TEXT CATEGORIZATION ; NETWORK CLASSIFIERS ; CLASSIFICATION ; ALGORITHMS ; RECOGNITION ; LVQ
收录类别SCI
语种英语
WOS记录号WOS:000277475100007
源URL[http://ir.ia.ac.cn/handle/173211/3065]  
专题自动化研究所_模式识别国家重点实验室_模式分析与学习团队
作者单位Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Jin, Xiao-Bo,Liu, Cheng-Lin,Hou, Xinwen. Regularized margin-based conditional log-likelihood loss for prototype learning[J]. PATTERN RECOGNITION,2010,43(7):2428-2438.
APA Jin, Xiao-Bo,Liu, Cheng-Lin,&Hou, Xinwen.(2010).Regularized margin-based conditional log-likelihood loss for prototype learning.PATTERN RECOGNITION,43(7),2428-2438.
MLA Jin, Xiao-Bo,et al."Regularized margin-based conditional log-likelihood loss for prototype learning".PATTERN RECOGNITION 43.7(2010):2428-2438.

入库方式： OAI收割

来源：自动化研究所

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