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  Gaussian Process Classification for Segmenting and Annotating Sequences

Altun, Y., Hofmann, T., & Smola, A. (2004). Gaussian Process Classification for Segmenting and Annotating Sequences. In R. Greiner, & D. Schuurmans (Eds.), ICML '04: Twenty-First International Conference on Machine Learning (pp. 25-32). New York, USA: ACM Press.

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 Creators:
Altun, Y1, Author              
Hofmann, T1, Author              
Smola, AJ1, Author              
Affiliations:
1External Organizations, ou_persistent22              

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 Abstract: Many real-world classification tasks involve the prediction of multiple, inter-dependent class labels. A prototypical case of this sort deals with prediction of a sequence of labels for a sequence of observations. Such problems arise naturally in the context of annotating and segmenting observation sequences. This paper generalizes Gaussian Process classification to predict multiple labels by taking dependencies between neighboring labels into account. Our approach is motivated by the desire to retain rigorous probabilistic semantics, while overcoming limitations of parametric methods like Conditional Random Fields, which exhibit conceptual and computational difficulties in high-dimensional input spaces. Experiments on named entity recognition and pitch accent prediction tasks demonstrate the competitiveness of our approach.

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 Dates: 2004-07
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1145/1015330.1015433
BibTex Citekey: 2740
 Degree: -

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Title: Twenty-First International Conference on Machine Learning (ICML 2004)
Place of Event: Banff, Canada
Start-/End Date: 2004-07-04 - 2004-07-08

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Title: ICML '04: Twenty-First International Conference on Machine Learning
Source Genre: Proceedings
 Creator(s):
Greiner, R, Editor
Schuurmans, D, Editor
Affiliations:
-
Publ. Info: New York, USA : ACM Press
Pages: - Volume / Issue: - Sequence Number: 4 Start / End Page: 25 - 32 Identifier: ISBN: 1-58113-838-5