Predicting structured data / edited by G�okhan Bak�r [and others].
Contributor(s): BakIr, G�okhan
| IEEE Xplore (Online Service) [distributor.] | MIT Press [publisher.] | Neural Information Processing Systems Foundation.
Material type: 
BookPublisher: Cambridge, Massachusetts : MIT Press, A2007Distributor: [Piscataqay, New Jersey] : IEEE Xplore, [2007]Description: 1 PDF (viii, 348 pages) : illustrations.Content type: text Media type: electronic Carrier type: online resourceISBN: 9780262255691.Subject(s): Data structures (Computer science) | Kernel functions | Computer algorithms | Machine learningGenre/Form: Electronic books.Additional physical formats: Print version: No titleOnline resources: Abstract with links to resource Also available in print.Summary: 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, G�Sokhan Bakir [no dot over i], Olivier Bousquet, Sumit Chopra, Corinna Cortes, Hal Daum� 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 P�rez-Cruz, Massimiliano Pontil, Marc'Aurelio Ranzato, Juho Rousu, Craig Saunders, Bernhard Sch�Solkopf, 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 G�Sokhan Bakir [no dot over i] is Research Scientist at the Max Planck Institute for Biological Cybernetics in T�ubingen, 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 Sch�Solkopf 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.
Collected papers based on talks presented at two Neural Information Processing Systems workshops.
Includes bibliographical references (pages 319-340) and index.
Restricted to subscribers or individual electronic text purchasers.
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, G�Sokhan Bakir [no dot over i], Olivier Bousquet, Sumit Chopra, Corinna Cortes, Hal Daum� 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 P�rez-Cruz, Massimiliano Pontil, Marc'Aurelio Ranzato, Juho Rousu, Craig Saunders, Bernhard Sch�Solkopf, 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 G�Sokhan Bakir [no dot over i] is Research Scientist at the Max Planck Institute for Biological Cybernetics in T�ubingen, 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 Sch�Solkopf 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.
Also available in print.
Mode of access: World Wide Web
Made available online by EBSCO.
Description based on PDF viewed 12/23/2015.
There are no comments for this item.