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Abstract:
Recently, Jaakkola and Haussler proposed a method for
constructing kernel functions from probabilistic models. Their so
called ``Fisher kernel'' has been combined with discriminative
classifiers such as SVM and applied successfully in e.g. DNA and
protein analysis. Whereas the Fisher kernel (FK) 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 FK and TOP. In
experiments our new discriminative TOP kernel compares favorably
to the Fisher kernel.
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