A compact neural network for training support vector machines

Yun Yang, Qiaochu He, Xiaolin Hu

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)193-198
Number of pages6
JournalNeurocomputing
Volume86
DOIs
StatePublished - Jun 1 2012
Externally publishedYes

Keywords

  • Analog circuits
  • Neural network
  • Quadratic programming
  • Support vector machine

ASJC Scopus subject areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence

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