Multivariate Density Estimation: a Support Vector Machine Approach
A Support Vector Machine (SVM) algorithm for multivariate density estimation is developed based on regularization principles and bounds on the convergence of empirical distribution functions. The algorithm is compared to Gaussian Mixture Models (GMMs). Our algorithm outperforms GMMs for data drawn from mixtures of gaussians in IR and IR. Our algorithm is also automated with respect to parameters. Copyright c Massachusetts Institute of Technology, 1998 This report describes research done within the Center for Biological and Computational Learning in the Department of Brain and Cognitive Sciences and at the Arti cial Intelligence Laboratory at the Massachusetts Institute of Technology. This research is sponsored by a grant from the O ce of Naval Research under contract No. N00014-93-13085. Additional support is provided by Eastman Kodak Company, Daimler-Benz AG, Siemens Corporate Research Inc., AT&T, Digital Equipment and Central Research Institute of Electric Power Industry.