In this paper we propose alternative methods to parameter selection techniques in order to build a kernel matrix for classification purposes using Support Vector Machines (SVMs). We describe several methods to build a unique kernel matrix from a collection of kernels built using a wide range of values for the unkown parameters. The proposed techniques have been successfully evaluated on a variety of artificial and real data sets. The new methods outperform the best individual kernel under consideration and they can be used as an alternative to the parameter selection problem in kernel methods.