Title Predicting structured data / edited by Gökhan Bakır [and others]

Published Cambridge, Mass. : MIT Press, ©2007

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Description 1 online resource (viii, 348 pages) : illustrations Series Advances in neural information processing systems Neural information processing series. Contents Measuring Similarity with Kernels -- Discriminative Models -- Modeling Structure via Graphical Models -- Joint Kernel Maps / Jason Weston [and others] -- Support Vector Machine Learning for Interdependent and Structured Output Spaces / Yasemin Altun, Thomas Hofmann, and Ioannis Tsochandiridis -- Efficient Algorithms for Max-Margin Structured Classification / Juho Rousu [and others] -- Discriminative Learning of Prediction Suffix Trees with the Perceptron Algorithm / Ofer Dekel, Shai Shalev-Shwartz, and Yoram Singer -- A General Regression Framework for Learning String-to-String Mappings / Corinna Cortes, Mehryar Mohri, and Jason Weston -- Learning as Search Optimization / Hal Daume III and Daniel Marcu -- Energy-Based Models / Yann LeCun [and others] -- Generalization Bounds and Consistency for Structured Labeling / David McAllester -- Kernel Conditional Graphical Models / Fernando Perez-Cruz, Zoubin Ghahramani, and Massimiliano Pontil -- Density Estimation of Structured Outputs in Reproducing Kernel Hilbert Spaces / Yasemin Altun and Alex J. Smola -- Gaussian Process Belief Propagation / Matthias W. Seeger 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, Gokhan Bakir, 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 Analysis COMPUTER SCIENCE/Machine Learning & Neural Networks Notes Collected papers based on talks presented at two Neural Information Processing Systems workshops Bibliography Includes bibliographical references (pages 319-340) and index Notes Master and use copy. Digital master created according to Benchmark for Faithful Digital Reproductions of Monographs and Serials, Version 1. Digital Library Federation, December 2002. http://purl.oclc.org/DLF/benchrepro0212 MiAaHDL English digitized 2010 HathiTrust Digital Library committed to preserve pda MiAaHDL Print version record Subject Machine learning. Computer algorithms. Kernel functions. Data structures (Computer science) Algorithms. Algorithms Machine Learning algorithms. COMPUTERS -- Enterprise Applications -- Business Intelligence Tools. COMPUTERS -- Intelligence (AI) & Semantics. Algorithms Computer algorithms Data structures (Computer science) Kernel functions Machine learning Lernen -- (Informatik) -- Kernel (Informatik) Lernen -- (Informatik) -- Strukturlogik. Strukturlogik -- Lernen (Informatik) Kernel -- (Informatik) -- Lernen (Informatik) Künstliche Intelligenz Maschinelles Lernen Form Electronic book Author BakIr, Gökhan. Neural Information Processing Systems Foundation. ISBN 9780262255691 0262255693 9781429499170 1429499176 9786612096075 6612096071 1282096079 9781282096073 9780262528047 0262528045

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