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Predicting Structured Data

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Gökhan BakIr 作者
Mit Press
譯者
2007-9 出版日期
360 頁數
$ 53.11 價格
HRD
叢書系列
9780262026178 圖書編碼

Predicting Structured Data 在線電子書 圖書標籤: 機器學習  數學  MachineLearning  AI  structured_output_learning  MIT  計算機科學  統計學習   


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Predicting Structured Data 在線電子書 epub 下載 mobi 下載 pdf 下載 txt 下載 2024

Predicting Structured Data 在線電子書 epub 下載 pdf 下載 mobi 下載 txt 下載 2024

Predicting Structured Data 在線電子書 pdf 下載 txt下載 epub 下載 mobi 下載 2024



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Predicting Structured Data 在線電子書 著者簡介

Gökhan Bakır 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 Managing Director of the Max Planck Institute for Biological Cybernetics in Tübingen, Germany. He is coauthor of Learning with Kernels (MIT Press, 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 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 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.


Predicting Structured Data 在線電子書 著者簡介


Predicting Structured Data 在線電子書 pdf 下載 txt下載 epub 下載 mobi 在線電子書下載

Predicting Structured Data 在線電子書 圖書描述

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, Gokhan Bakir [no dot over i], Olivier Bousquet, Sumit Chopra, Corinna Cortes, Hal Daume 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 Perez-Cruz, Massimiliano Pontil, Marc'Aurelio Ranzato, Juho Rousu, Craig Saunders, Bernhard Scholkopf, 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 Gokhan Bakir [no dot over i] is Research Scientist at the Max Planck Institute for Biological Cybernetics in Tubingen, 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 Scholkopf 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.

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