Predicting Structured Data

Предна корица
MIT Press, 2007 - 348 страници

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, 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.

 

Съдържание

II Structured Prediction Based on Discriminative Models
65
III Structured Prediction Using Probabilistic Models
263
References
319
Contributors
341
Index
345
Авторско право

Други издания - Преглед на всички

Predicting Structured Data
Neural Information Processing Systems Foundation
Фрагменти - 2007
Predicting Structured Data
Gökhan Bakir,Bernhard Schölkopf,Ben Taskar,Alexander J. Smola
Няма достъп - 2007

Често срещани думи и фрази

algorithm answer applications approach approximation architecture assume bound called chapter classification clique complexity components compute conditional consider consists constraints correct corresponding defined definite denote dependencies described different distribution dual efficient energy error estimation et al example exists exponential factor Figure first framework given gives gradient graph independent inference input iteration joint kernel labels learning linear loss function machine margin Markov matrix methods minimization networks node Note objective observed obtain optimization output parameters path perceptron performance pitch accent positive possible pre-image prediction present probability problem proposed random recognition regression representation represented requires respect sample separation sequence similar simple solution solve space standard statistical strings structured task theorem tree update variables vector weight

Библиография

ЗаглавиеPredicting Structured Data
Advances in neural information processing systems
Neural information processing series
АвторNeural Information Processing Systems Foundation
РедакторGökhan BakIr
Изданиеилюстрирано
ИздателMIT Press, 2007
ISBN0262026171, 9780262026178
Брой страници348 страници
  
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