Density Estimation of Structured Outputs in Reproducing Kernel Hilbert Spaces

Altun, Y; Smola, A

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Density Estimation of Structured Outputs in Reproducing Kernel Hilbert Spaces

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https://ieeexplore.ieee.org/document/6270204?tp=&arnumber=6270204
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Altun, Y., & Smola, A. (2007). Density Estimation of Structured Outputs in Reproducing Kernel Hilbert Spaces. In G. Bakır, T. Hofmann, B. Schölkopf, A. Smola, B. Taskar, & S. Vishwanathan (Eds.), Predicting Structured Data (pp. 283-300). Cambridge, MA, USA: MIT Press.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-CC03-E

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.