.png){kind=icon}
返回
Regularized margin-based conditional log-likelihood loss for prototype learning
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patcog.2010.01.013.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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.
Keyword:
Prototype learning
Conditional log-likelihood loss
Log-likelihood of margin (LOGM)
Regularization
Distance metric learning
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.6
论文数:
1.3W
被引数:
4.5W
机构
引用论文
Coordination complexes of niobium and tantalum. V. Eight-coordinated di- and triperoxoniobates(V) and -tantalates(V) with some nitrogen and oxygen bidentate ligands铌和钽的配合物。五。具有一些氮和氧双齿配体的八配位的二过氧化物和三过氧化物铌酸盐 (V) 和钽酸盐 (V)
PREAI
分享收藏Determination of the spread parameter in the Gaussian kernel for classification and regression.png)
NEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
分享收藏
分享收藏Initialization insensitive LVQ algorithm based on cost-function adaptationPATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
分享收藏
查看更多内容