PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Density estimation of Structured Outputs in RKHS
Y. Altun and Alex Smola
In: Predicting Structured Data (2007) MIT Press . ISBN 0-262-02617-1

Abstract

In this paper we study the problem of estimating conditional probability distributions for structured output prediction tasks in Reproducing Kernel Hilbert Spaces. More specically, we prove decomposition results for undirected graphical models, give constructions for kernels, and show connections to Gaussian Process classi- cation. Finally we present ecient means of solving the optimization problem and apply this to label sequence learning. Experiments on named entity recognition and pitch accent prediction tasks demonstrate the competitiveness of our approach.

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EPrint Type:Book Section
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:3982
Deposited By:Alex Smola
Deposited On:25 February 2008