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Predicting Structured Data -

English
2007-07-27
โ‚ฌ60.25 โ‚ฌ75.31

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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: ... Full description

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Description

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.ContributorsYasemin Altun, Gรถkhan Bakir, Olivier Bousquet, Sumit Chopra, Corinna Cortes, Hal Daumรฉ 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 Pรฉrez-Cruz, Massimiliano Pontil, Marc'Aurelio Ranzato, Juho Rousu, Craig Saunders, Bernhard Schรถlkopf, 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

More Information

Publisher MIT Press
Release year 2007
Cover type Softcover
EAN 9780262528047
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โ‚ฌ60.25 โ‚ฌ75.31