Optimal Hyper-parameter Decision Method for Support Vector Machine Using Improved Response Surface Method
Takayuki Sekine, Eitaro Aiyoshi
Support Vector Machine (SVM) has been studied actively as one of discriminators for a large number of data. In order to obtain a high identification rate, it is necessary to decide the best parameters of the SVM, and the problem to decide the parameters are formulated as an optimization problem, where the objective function is not described by mathematical formula, and a function call to evaluate the objective function value requires calculation quantity. In this study, to break through the problems, it is considered to adopt the Response Surface Method (RSM) with meta-heuristic algorithm to solve the SVM’s parameter optimization problem.