Multi-Class Classification with Maximum Margin Multiple Kernel

We present a new algorithm for multi-class classification with multiple kernels. Our algorithm is based on a natural notion of the multi-class margin of a kernel. We show that larger values of this quantity guarantee the existence of an accurate multi-class predictor and also define a family of multiple kernel algorithms based on the maximization of the multi-class margin of a kernel (M^3K). We present an extensive theoretical analysis in support of our algorithm, including novel multi-class Rademacher complexity margin bounds. Finally, we also report the results of a series of experiments with several data sets, including comparisons where we improve upon the performance of state-of-the-art algorithms both in binary and multi-class classification with multiple kernels.