Directly Optimised Support Vector Machines for Classification and Regression

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Drezet, P. ; Harrison, R.F. (1998)
  • Publisher: Department of Automatic Control and Systems Engineering
  • Subject:
    acm: ComputingMethodologies_PATTERNRECOGNITION

A new method of implementing Support Vector learning algorithms for classification and regression is presented which deals with problems of over-defined solutions and excessive complexity. Classification problems are solved with the minimum number of support vectors, irrespective of over-lapping training data. Support vector regression can be solved as a sparse solution, without requiring an e-insensitive zone. The optimisation method is generalised to include control of sparsity for both support vector classification and regression.
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