title: A new discriminative kernel from probabilistic models.
abstract: Recently, Jaakkola and Haussler (1999) proposed a method for constructing kernel functions from probabilistic models. Their so-called Fisher kernel has been combined with discriminative classifiers such as support vector machines and applied successfully in, for example, DNA and protein analysis. Whereas the Fisher kernel is calculated from the marginal log-likelihood, we propose the TOP kernel derived; from tangent vectors of posterior log-odds. Furthermore, we develop a theoretical framework on feature extractors from probabilistic models and use it for analyzing the TOP kernel. In experiments, our new discriminative TOP kernel compares favorably to the Fisher kernel.
authors.affiliation: AIST Computational Biology Research Center, Koto-ku, Tokyo, 135-0064, Japan. koji.tsuda@aist.go.jp
authors.forename: Koji
authors.initials: K
authors.lastname: Tsuda
authors.forename: Motoaki
authors.initials: M
authors.lastname: Kawanabe
authors.forename: Gunnar
authors.initials: G
authors.lastname: Rätsch
authors.forename: Sören
authors.initials: S
authors.lastname: Sonnenburg
authors.forename: Klaus-Robert
authors.initials: KR
authors.lastname: Müller
mesh_terms: D008959:Models, Neurological; D015233:Models, Statistical; D016571:Neural Networks, Computer; D011506:Proteins / Q000145:classification
chemical_list: D011506:Proteins