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Synopsis
State-of-the-art algorithms and theory in a novel domain of machine learning, prediction when the output has structure.
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.
Contributors
Yasemin Altun, Gökhan Bakir, Olivier Bousquet, Sumit Chopra, Corinna Cortes, Hal Daumé III, Ofer Dekel, Zoubin Ghahramani, Raia Hadsell, Thomas Hofmann, Fu Jie Huang, Yann LeCun, Tobias Mann, Daniel Marcu, David McAllester, Mehryar Mohri, William Stafford Noble, Fernando Pérez-Cruz, Massimiliano Pontil, Marc'Aurelio Ranzato, Juho Rousu, Craig Saunders, Bernhard Schölkopf, Matthias W. Seeger, Shai Shalev-Shwartz, John Shawe-Taylor, Yoram Singer, Alexander J. Smola, Sandor Szedmak, Ben Taskar, Ioannis Tsochantaridis, S.V.N Vishwanathan, Jason Weston
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About the Author
Gökhan Bakir is Research Scientist at the Max Planck Institute for Biological Cybernetics in Tübingen, Germany.
"About this title" may belong to another edition of this title.
- PublisherMIT Press
- Publication date2007
- ISBN 10 0262528045
- ISBN 13 9780262528047
- BindingPaperback
- LanguageEnglish
- Number of pages362
- EditorBakir Gokhan, Hofmann Director Of Engineering At Google's Engineering Center In Zurich Adjunct Associate Professor Of Com Thomas, Scholkopf Bernhard
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