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Predicting Structured Data




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.

Author(s): Bakir, GH. and Hofmann, T. and Schölkopf, B. and Smola, AJ. and Taskar, B. and Vishwanathan, SVN.
Pages: 360
Year: 2007
Month: September
Day: 0
Series: Advances in neural information processing systems
Publisher: MIT Press

Department(s): Empirical Inference
Bibtex Type: Book (book)

Address: Cambridge, MA, USA
Digital: 0
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: Web


  title = {Predicting Structured Data},
  author = {Bakir, GH. and Hofmann, T. and Sch{\"o}lkopf, B. and Smola, AJ. and Taskar, B. and Vishwanathan, SVN.},
  pages = {360},
  series = {Advances in neural information processing systems},
  publisher = {MIT Press},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Cambridge, MA, USA},
  month = sep,
  year = {2007},
  month_numeric = {9}