Predicting Structured Data

From Neural Information Processing series

Predicting Structured Data

Edited by Gökhan BakIr, Thomas Hofmann, Bernhard Schölkopf, Alexander J. Smola, Ben Taskar and S.V.N Vishwanathan

State-of-the-art algorithms and theory in a novel domain of machine learning, prediction when the output has structure.
Hardcover $47.00 X £32.95
Paperback $43.00 X £34.00

Overview

Author(s)

Summary

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.

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

Hardcover

Out of Print ISBN: 9780262026178 360 pp. | 8 in x 10 in 61 fig/19 tbls illus.

Paperback

$43.00 X | £34.00 ISBN: 9780262528047 360 pp. | 8 in x 10 in 61 fig/19 tbls illus.

Editors

Gökhan BakIr

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

Thomas Hofmann

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

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

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

Ben Taskar

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

S.V.N Vishwanathan

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.