An efficient formulation of sparsity controlled support vector regression
نویسندگان
چکیده
Support Vector Regression (SVR) is a kernel based regression method capable of implementing a variety of regularisation techniques. Implementation of SVR usually follows a dual optimisation technique which includes Vapnik's -insensitive zone. The number of terms in the resulting SVR approximation function is dependent on the size of this zone, but improving sparsity by increasing the size of this zone adversely e ects precision. We describe an e cient method of formulating SVR without an -insensitive zone, that selects a minimum support set for the terms of the approximator. Sparsity can then be traded for increased training error and/or decreased SV regularisation.
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