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

Collected papers based on talks presented at two Neural Information Processing Systems workshops.

Includes bibliographical references (pages 319-340) and index.

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.

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English.

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

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