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  Predicting Structured Data

Bakir, G., Hofmann, T., Schölkopf, B., Smola, A., Taskar, B., & Vishwanathan, S. (2007). Predicting Structured Data. Cambridge, MA, USA: MIT Press.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-CBAD-7 Version Permalink: http://hdl.handle.net/21.11116/0000-0003-ECE8-E
Genre: Book

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 Creators:
Bakir, GH1, 2, Author              
Hofmann, T, Author              
Schölkopf, B1, 2, Author              
Smola , AJ, Author
Taskar, B, Author
Vishwanathan, SVN, Author
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: Machine learning develops intelligent computer systems that are able to generalize from previously seen examples. A new domain of machine learning, in which the prediction must satisfy the additional constraints found in structured data, poses one of machine learning’s greatest challenges: learning functional dependencies between arbitrary input and output domains. This volume presents and analyzes the state of the art in machine learning algorithms and theory in this novel field. The contributors discuss applications as diverse as machine translation, document markup, computational biology, and information extraction, among others, providing a timely overview of an exciting field.

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 Dates: 2007-09
 Publication Status: Published in print
 Pages: 360
 Publishing info: Cambridge, MA, USA : MIT Press
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: 4269
ISBN: 978-0-262-02617-8
 Degree: -

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Title: Advances in neural information processing systems
Source Genre: Series
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Publ. Info: Cambridge, MA, USA : MIT Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: - Identifier: -