Bilinear Grid Search Strategy Based Support Vector Machines Learning Method

Support Vector Machines (SVM) learning can be used to construct classification models of high accuracy. However, the performance of SVM learning should be improved. This paper proposes a bilinear grid search method to achieve higher computation efficiency in choosing kernel parameters (C, γ) of SVM with RBF kernel. Experiments show that the proposed method retains the advantages of a small number of training SVMs of bilinear search and the high prediction accuracy of grid search. It has been proved that bilinear grid search method (BGSM) is an effective way to train SVM with RBF kernel. With the application of BGSM, the protein secondary structure prediction can obtain a better learning accuracy compared with other related algorithms.