Density estimation of Structured Outputs in RKHS
Y. Altun and Alex Smola
Predicting Structured Data
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.