A Parallel Way to Select the Parameters of SVM Based on the Ant Optimization Algorithm

A large number of experimental data shows that Support Vector Machine (SVM) algorithm has obvious a large advantages in text classification, handwriting recognition, image classification, bioinformatics, and some other fields. To some degree, the optimization of SVM depends on its kernel function and Slack variable, the determinant of which is its parameters δ and c in the classification function. That is to say, to optimize the SVM algorithm, the optimization of the two parameters play a huge role. Ant Colony Optimization (ACO) is optimization algorithm which simulate ants to find the optimal path. In the available literature, we mix the ACO algorithm and Parallel algorithm together to find a well parameters. Keyword: SVM, Parameters, ACO, OpenCL, Parallel I. SUPPORT VECTOR CLASSIFICATION AND PARAMETERS SVM is based on the principle of structural risk minimization,using limited training samples to obtain the higher generalization ability of decision function. Suppose a sample set (xi, yi) , where i = 1, 2...N means the number of training samples, x∈R means the sample characteristics, y ∈ {+1,−1} means the sample classification. SVM Classification function: y = ωx+ b (ω means weight vector,b means setover)