A new method for sparsity control in support vector classification and regression

Abstract 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 a minimum number of support vectors, irrespective of the degree of overlap in the training data. Support vector regression can deliver a sparse solution, without requiring Vapnik's e -insensitive zone. This paper generalises sparsity control for both support vector classification and regression. The novelty in this work is in the method of achieving a sparse support vector set which forms a minimal basis for the prediction function.