On Line LS-SVM for Classification Kim, Daehak; Oh, KwangSik; Shim, Jooyong;
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.
Least Squares Support Vector Machine;Classification;Lagrange multiplier;Inverse matrix;On line training;
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