Predicting Structured Data
Published on 1. September 2007
Book
Hardback
360 pages
978-0-262-02617-8 (ISBN)
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 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.
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.
More details
Series
Language
English
Place of publication
Cambridge, Mass.
United States
Publishing group
MIT Press Ltd
Target group
Professional and scholarly
Interest Age: From 18 years
Illustrations
19 Tabellen, 61 Schaubilder
61 fig/19 tbls illus.
Dimensions
Height: 254 mm
Width: 203 mm
Thickness: 0 mm
Weight
907 gr
ISBN-13
978-0-262-02617-8 (9780262026178)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Goekhan BakIr | Thomas Hofmann | Bernhard Schoelkopf
Predicting Structured Data
Book
07/2007
MIT Press
€40.70
Shipment within 10-20 days
Persons
Gökhan Bakır is Research Scientist at the Max Planck Institute for Biological Cybernetics in Tübingen, Germany.
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.
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.
Editor
Research ScientistGoogle
Director of Engineering at Google's Engineering Center in Zurich, Adjunct Associate Professor of ComGoogle Switzerland
Director of the Max Planck Institute for Intelligent in Tuebingen, Germany, Professor for Machine LeaMax Planck Institute for Intelligent Systems
Senior Researcher in the Statistical Machine Learning Program, National ICT Australia with an adjunc