Learning rates of support vector machine classifier for density level detection

被引：4

作者：

Cao, Feilong ^{[1
]}

Xing, Xing ^{[1
]}

Zhao, Jianwei ^{[1
]}

机构：

[1] China Jiliang Univ, Dept Informat & Math Sci, Hangzhou 310018, Zhejiang, Peoples R China

来源：

NEUROCOMPUTING | 2012年 / 82卷

基金：

中国国家自然科学基金;

关键词：

Learning rates; Support vector machines; Density level detection; Anomaly detection; SOFT MARGIN CLASSIFIERS; CONSISTENCY;

D O I：

10.1016/j.neucom.2011.10.032

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, we consider the learning rates of support vector machines (SVMs) classifier for density level detection (DLD) problem. Using an established classification framework, we get error decomposition which consists of regularization error and sample error. Based on the decomposition, we obtain learning rates of SVMs classifier for OLD under some assumptions. Crown Copyright (c) 2011 Published by Elsevier BM. All rights reserved.

引用

页码：84 / 90

页数：7

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