On Sequence Kernels for SVM classification of sets of...

Google Tech Talks April 10, 2007 ABSTRACT Support Vector Machines (SVMs) have become one of the most popular tools for discriminative classification of static data. However, research in SVM classification of dynamic (continuous) data has gained in interest only recently. In this presentation, I first give an overview of existing sequence kernels for classification of sets of vectors. I then present a new family of sequence kernels that generalizes the Generalized Linear Discriminant Sequence (GLDS) kernel. As opposed to GLDS, the new sequence kernels allow implicit normalized expansions in a high/infinite-dimensional feature space (FS). Moreover, they induce a Mahalanobis distance in the FS which...