Letters: A compact neural network for training support vector machines

Authors:
Yun Yang;Qiaochu He;Xiaolin Hu
Affiliations:
Department of Mathematical Science, Tsinghua University, Beijing 100084, China;Department of Automotive Engineering, Tsinghua University, Beijing 100084, China;State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology (TNList), Department of Computer Science and Technology, Tsinghua Un ...
Venue:
Neurocomputing
Year:
2012

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Abstract

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.