PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Predicting Structured Data
G.H. BakIr, T. Hofmann, B. Schölkopf, A.J. Smola, B. Taskar and S.V.N. Vishwanathan
(2007) MIT Press , Cambridge, MA, USA .

Abstract

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.

EPrint Type:Book
Additional Information:http://mitpress.mit.edu/catalog/item/default.asp?ttype=2&tid=11332
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:4021
Deposited By:Bernhard Schölkopf
Deposited On:22 February 2008