The kernelHMM: Learning Kernel Combinations in Structured Output Domains

  • Volker Roth
  • Bernd Fischer
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4713)

Abstract

We present a model for learning convex kernel combinations in classification problems with structured output domains. The main ingredient is a hidden Markov model which forms a layered directed graph. Each individual layer represents a multilabel version of nonlinear kernel discriminant analysis for estimating the emission probabilities. These kernel learning machines are equipped with a mechanism for finding convex combinations of kernel matrices. The resulting kernelHMM can handle multiple partial paths through the label hierarchy in a consistent way. Efficient approximation algorithms allow us to train the model to large-scale learning problems. Applied to the problem of document categorization, the method exhibits excellent predictive performance.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Volker Roth
    • 1
  • Bernd Fischer
    • 1
  1. 1.ETH Zurich, Institute of Computational Science, Universität-Str. 6, CH-8092 Zurich 

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