Communications for Statistical Applications and Methods

The Korean Statistical Society (한국통계학회)
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On Line LS-SVM for Classification

  • Kim, Daehak (Department of Statistical Information, Catholic University of Daegu) ;
  • Oh, KwangSik (Department of Statistical Information, Catholic University) ;
  • Shim, Jooyong (Department of Statistical Information, Catholic University of Daegu)
  • Published : 2003.08.01
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Abstract

In this paper we propose an on line training method for classification based on least squares support vector machine. Proposed method enables the computation cost to be reduced and the training to be peformed incrementally, With the incremental formulation of an inverse matrix in optimization problem, current information and new input data can be used for building the new inverse matrix for the estimation of the optimal bias and Lagrange multipliers, so the large scale matrix inversion operation can be avoided. Numerical examples are included which indicate the performance of proposed algorithm.

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References

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