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9780262026178: Predicting Structured Data (Neural Information Processing Series)

Sinopsis

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, Gokhan Bakir [no dot over i], Olivier Bousquet, Sumit Chopra, Corinna Cortes, Hal Daume 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 Perez-Cruz, Massimiliano Pontil, Marc'Aurelio Ranzato, Juho Rousu, Craig Saunders, Bernhard Scholkopf, 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 Gokhan Bakir [no dot over i] is Research Scientist at the Max Planck Institute for Biological Cybernetics in Tubingen, Germany. Thomas Hofmann is a Director of Engineering at Google's Engineering Center in Zurich and Adjunct Associate Professor of Computer Science at Brown University. Bernhard Scholkopf is Director of the Max Planck Institute for Biological Cybernetics and Professor at the Technical University Berlin. Alexander J. Smola is Senior Principal Researcher and Machine Learning Program Leader at National ICT Australia/Australian National University, Canberra. Ben Taskar is Assistant Professor in the Computer and Information Science Department at the University of Pennsylvania. S. V. N. Vishwanathan is Senior Researcher in the Statistical Machine Learning Program, National ICT Australia with an adjunct appointment at the Research School for Information Sciences and Engineering, Australian National University.

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Gökhan Bakir is Research Scientist at the Max Planck Institute for Biological Cybernetics in Tübingen, Germany.

Thomas Hofmann is a Director of Engineering at Google's Engineering Center in Zurich and Adjunct Associate Professor of Computer Science at Brown University.

Bernhard Schölkopf is Director at the Max Planck Institute for Intelligent Systems in Tübingen, Germany. He is coauthor of Learning with Kernels (2002) and is a coeditor of Advances in Kernel Methods: Support Vector Learning (1998), Advances in Large-Margin Classifiers (2000), and Kernel Methods in Computational Biology (2004), all published by the MIT Press.

Alexander J. Smola is Senior Principal Researcher and Machine Learning Program Leader at National ICT Australia/Australian National University, Canberra.

Ben Taskar is Assistant Professor in the Computer and Information Science Department at the University of Pennsylvania.

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9780262528047: Predicting Structured Data

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ISBN 10:  0262528045 ISBN 13:  9780262528047
Editorial: MIT Press, 2007
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Publicado por MIT Press, 2007
ISBN 10: 0262026171 ISBN 13: 9780262026178
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Gokhan H. Bakir; Thomas Hofmann; Bernhard Scholkopf; Alexander J. Smola; Ben Taskar; S. V. N. Vishwanathan
Publicado por The MIT Press, Cambridge, MA, 2007
ISBN 10: 0262026171 ISBN 13: 9780262026178
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Hardcover. Condición: Fine. Estado de la sobrecubierta: Near Fine. 1st Edition. First edition, the uncommon issue in full cloth and dust jacket. Pale green cloth covered boards with gilt titles to spine, neat two words "Seung Lab" to front pastedown, no other markings. Publisher's illustrated dust jacket with a 1 inch long closed tear to rear panel and a shorter closed tear to head of spine, minor edgewear, now covered in protective mylar jacket protector. Uncommon in hardcover. Nº de ref. del artículo: 003177

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Publicado por The MIT Press, 2007
ISBN 10: 0262026171 ISBN 13: 9780262026178
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