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Predicting Structured Data (Advances in Neural Information Processing Systems)

by Gokhan (edt) Bakir

Predicting Structured Data (Advances in Neural Information Processing Systems) Cover

 

Synopses & Reviews

Publisher Comments:

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.

Synopsis:

State-of-the-art algorithms and theory in a novel domain of machine learning, prediction when the output has structure.

About the Author

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 [no dot over i], 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 Gökhan Bakir [no dot over i] is Research Scientist at the Max Planck Institute for Biological Cybernetics in Tübingen, 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 Schölkopf is Director of the Max Planck Institute for Biological Cybernetics and Professor at the Technical University Berlin. 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 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.

Product Details

ISBN:
9780262026178
Author:
Bakir, Gokhan (edt)
Publisher:
MIT Press (MA)
Editor:
Bakir, Golchan
Editor:
Hofmann, Thomas
Editor:
Scholkopf, Bernhard
Editor:
Smola, Alexander J.
Editor:
Schlkopf, Bernhard
Author:
Mann, Tobias
Author:
BakIr, Gökhan
Author:
Mohri, Mehryar
Author:
Taskar, Ben
Author:
Hofmann, Thomas
Author:
Ghahramani, Zoubin
Author:
Noble, William Stafford
Author:
Pérez-Cruz, Fernando
Author:
Scholkopf, Bernhard
Author:
lkopf, Bernhard
Author:
Huang, Fu Jie
Author:
Rousu, Juho
Author:
Szedmak, Sander
Author:
Saunders, Craig
Author:
Weston, Jason
Author:
ouml
Author:
sch
Author:
Altun, Yasemin
Author:
Dekel, Ofer
Author:
Seeger, Matthias
Author:
Shalev-Shwartz, Shai
Author:
Hal Daumé, III
Author:
khan H.
Author:
McAllester, David A.
Author:
Ranzato, Marc'Aurelio
Author:
Massachusetts Institute of Technology
Author:
Daumé, Hal, III
Author:
&
Author:
Bakir, G
Author:
Singer, Yoram
Author:
Lecun, Yann
Author:
Tsochandiridis, Ioannis
Author:
Shawe-Taylor, John
Author:
Hadsell, Raia
Author:
Vishwanathan, S. V. N.
Author:
Smola, Alexander J.
Author:
Marcu, Daniel
Author:
Cortes, Corinna
Author:
Pontil, Massimiliano
Author:
Bakir, Golchan
Author:
Bakir, Gökhan H.
Author:
Taskar, Benjamin
Author:
Bousquet, Olivier
Author:
Chopra, Sumit
Location:
Cambridge
Subject:
Neural Networks
Subject:
Machine Theory
Subject:
Programming - Algorithms
Subject:
Data structures (computer science)
Subject:
Computer algorithms
Subject:
Networking - General
Copyright:
Series:
Neural Information Processing series Predicting Structured Data
Publication Date:
20070727
Binding:
HARDCOVER
Grade Level:
from 17
Language:
English
Illustrations:
61 fig/19 tbls illus.
Pages:
360
Dimensions:
10 x 8 in

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Related Aisles

Predicting Structured Data (Advances in Neural Information Processing Systems) New Hardcover
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$47.50 In Stock
Product details 360 pages Mit Press - English 9780262026178 Reviews:
"Synopsis" by , State-of-the-art algorithms and theory in a novel domain of machine learning, prediction when the output has structure.
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