A new discriminative kernel from probabilistic models.
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.
Tsuda
Koji
K
AIST Computational Biology Research Center, Koto-ku, Tokyo, 135-0064, Japan. koji.tsuda@aist.go.jp
Kawanabe
Motoaki
M
Rätsch
Gunnar
G
Sonnenburg
Sören
S
Müller
Klaus-Robert
KR
D008959:Models, Neurological; D015233:Models, Statistical; D016571:Neural Networks, Computer; D011506:Proteins / Q000145:classification
D011506:Proteins