Support Vector Machine with Customized Kernel

In the past two decades, Support Vector Machine (SVM) has become one of the most famous classification techniques. The optimal parameters in an SVM kernel are normally obtained by cross validation, which is a time-consuming process. In this paper, we propose to learn the parameters in an SVM kernel while solving the dual optimization problem. The new optimization problem can be solved iteratively as follows: (a) Fix the parameters in an SVM kernel; solve the variables αi in the dual optimization problem. (b) Fix the variables αi; solve the parameters in an SVM kernel by using the Newton-Raphson method. It can be shown that (a) can be optimized by using standard methods in training the SVM, while (b) can be solved iteratively by using the Newton-Raphson method. Experimental results conducted in this paper show that our proposed technique is feasible in practical pattern recognition applications.