Multiple-kernel-learning-based extreme learning machine for classification design

The extreme learning machine (ELM) is a new method for using single hidden layer feed-forward networks with a much simpler training method. While conventional kernel-based classifiers are based on a single kernel, in reality, it is often desirable to base classifiers on combinations of multiple kernels. In this paper, we pro- pose the issue of multiple-kernel learning (MKL) for ELM by formulating it as a semi-infinite linear pro- gramming. We further extend this idea by integrating with techniques of MKL. The kernel function in this ELM formulation no longer needs to be fixed, but can be automatically learned as a combination of multiple ker- nels. Two formulations of multiple-kernel classifiers are proposed. The first one is based on a convex combination of the given base kernels, while the second one uses a convex combination of the so-called equivalent kernels. Empirically, the second formulation is particularly com- petitive. Experiments on a large number of both toy and real-world data sets (including high-magnification sam- pling rate image data set) show that the resultant classifier is fast and accurate and can also be easily trained by simply changing linear program.