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Predicting Structured Data (Advances in Neural Information Processing Systems) by Gökhan H. Bakir, Thomas Hofmann, Bernhard Schölkopf, Alexander J. Smola, Ben Taskar, S. V. N. Vishwa is a nonfiction available to read on EtoBox.

What is Predicting Structured Data (Advances in Neural Information Processing Systems) about?

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, Gökhan Bakir, 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, Ma

Who reads Predicting Structured Data (Advances in Neural Information Processing Systems)?

Author

Gökhan H. Bakir, Thomas Hofmann, Bernhard Schölkopf, Alexander J. Smola, Ben Taskar, S. V. N. Vishwa

Publisher

The MIT Press

Published

2007

Language

EN

ISBN

9781429499170

Category

nonfiction

Subjects

Science, Mathematics, Programming

Rating

3.71 / 5 (7 ratings)

Updated

2026-03-14