Predicting Structured Data
MIT Press (Verlag)
978-0-262-52804-7 (ISBN)
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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, Goekhan Bakir, 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 Schoelkopf, 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
Goekhan Bakir 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 Schoelkopf is Director at the Max Planck Institute for Intelligent Systems in Tubingen, 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. S. V. N. Vishwanathan is an Assistant Professor of Statistics and Computer Science at Purdue University and 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. Jason Weston is a Research Scientist at NEC Labs America. Goekhan Bakir is Research Scientist at the Max Planck Institute for Biological Cybernetics in Tubingen, Germany. Bernhard Schoelkopf is Director at the Max Planck Institute for Intelligent Systems in Tubingen, 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. Thomas Hofmann is a Director of Engineering at Google's Engineering Center in Zurich and Adjunct Associate Professor of Computer Science at Brown University. Mehryar Mohri is Professor of Computer Science at New York University's Courant Institute of Mathematical Sciences and a Research Consultant at Google Research. Daniel Marcu is Director of Strategic Initiatives at the Information Sciences Institute and Research Associate Professor in the Department of Computer Science at the University of Southern California. Yann LeCun is Head of the Image Processing Research Department at AT&T Labs-Research. David A McAllester is an Assistant Professor of Computer Science at MIT. Zoubin Ghahramani is Lecturer in the Gatsby Computational Neuroscience Unit at University College London. Alexander J. Smola is Senior Principal Researcher and Machine Learning Program Leader at National ICT Australia/Australian National University, Canberra.
| Erscheint lt. Verlag | 27.7.2007 |
|---|---|
| Reihe/Serie | Neural Information Processing series |
| Zusatzinfo | 61 fig/19 tbls illus. |
| Verlagsort | Cambridge, Mass. |
| Sprache | englisch |
| Maße | 203 x 254 mm |
| Themenwelt | Informatik ► Theorie / Studium ► Algorithmen |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| ISBN-10 | 0-262-52804-5 / 0262528045 |
| ISBN-13 | 978-0-262-52804-7 / 9780262528047 |
| Zustand | Neuware |
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