Predicting Structured Data

Gokhan Bakir (u. a.)

ISBN 10: 0262528045 / ISBN 13: 9780262528047

Published by MIT Press, 2007

About this Item

Predicting Structured Data | Gokhan Bakir (u. a.) | Taschenbuch | Kartoniert / Broschiert | Englisch | 2007 | MIT Press | EAN 9780262528047 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.

Seller Inventory # 117631103

Bibliographic Details

• Title: Predicting Structured Data
• Publisher: MIT Press
• Publication Date: 2007
• Language: English
• ISBN 10: 0262528045
• ISBN 13: 9780262528047
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 254W x 203H x 20D millimeters 254 millimeters width by 203 millimeters height by 20 millimeters depth
• Weight: 776 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

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

About the Author

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