ENGLISH

Predicting Structured Data

By Bernhard Schölkopf , Alexander J. Smola , Gökhan H. Bakir , Thomas Hofmann , Ben Taskar , S. V. N. Vishwanathan

Book information

Publisher
MIT Press
Year
2007
ISBN
0262026171, 9780262026178, 9781429499170
LCC
Q325.5 .P74 2007
Open Library ID
OL10236789M
Language
english
Format
PDF
Filesize
3 MB (2887293 bytes)
Series
Advances in neural information processing systems
Pages
361\361
Library
Kolxo3
Time added
2009-12-04 00:34:26

Description

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 Bakır, 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, and Jason Weston

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