Model Selection for Support Vector Machines via Adaptive Step-Size Tabu Search
The generalisation properties of a support vector classification network are typically governed by a regularisation parameter, C, and a small number of parameters specifying the kernel function. The process by which the optimal values of these parameters are obtained is known as model selection. This paper describes an automated model selection procedure based on minimisation of an upper bound on the leave-one-out cross-validation error, via a simple tabu search strategy with adaptive step size adjustment.
KeywordsSupport Vector Machine Support Vector Tabu Search Model Selection Procedure Tabu Search Heuristic
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