Evaluation of a hybrid method for constructing multiple SVM kernels
Dana Simian, Florin Stoica · Annual Conference on Computers · 2009
In this paper we evaluate the performance of many multiple SVM kernels obtained using a hybrid algorithm. The purpose of our algorithm is to optimize the construction of multiple SVM kernels used in classification tasks. We compare the results obtained using different types of simple kernels and we characterize the behavior of the multiple kernel related to the composition operations +,* and exp and simple kernel types. We use many data sets in order to correlate the performance of our algorithm with the type of the classified data.