A compact neural network for training support vector machines

被引：10

作者：

Yang, Yun ^{[2
]}

He, Qiaochu ^{[3
]}

Hu, Xiaolin ^{[1
]}

机构：

[1] Tsinghua Univ, State Key Lab Intelligent Technol & Syst, Tsinglrua Natl Lab Informat Sci & Technol TNList, Dept Comp Sci & Technol, Beijing 100084, Peoples R China

[2] Tsinghua Univ, Dept Math Sci, Beijing 100084, Peoples R China

[3] Tsinghua Univ, Dept Automot Engn, Beijing 100084, Peoples R China

来源：

NEUROCOMPUTING | 2012年 / 86卷

基金：

中国国家自然科学基金;

关键词：

Neural network; Support vector machine; Quadratic programming; Analog circuits; VARIATIONAL-INEQUALITIES; TUTORIAL;

D O I：

10.1016/j.neucom.2012.01.004

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

An analog neural network architecture for support vector machine (SVM) learning is presented in this letter, which is an improved version of a model proposed recently in the literature with additional parameters. Compared with other models, this model has several merits. First, it can solve SVMs (in the dual form) which may have multiple solutions. Second, the structure of the model enables a simple circuit implementation. Third, the model converges faster than its predecessor as indicated by empirical results. (C) 2012 Elsevier B.V. All rights reserved.

引用

页码：193 / 198

页数：6

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