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.
%0 Book
%1 BakirHofmannEtAl2007
%B Neural Information Processing
%C Cambridge, MA
%D 2007
%E Bakir, Gökhan H.
%E Hofmann, Thomas
%E Schölkopf, Bernhard
%E Smola, Alexander J.
%E Taskar, Ben
%E Vishwanathan, S. V. N.
%I MIT Press
%K 01801 105 book shelf mitpress ai learn knowledge information processing
%T Predicting Structured Data
%X 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.
%@ 978-0-262-02617-8
@book{BakirHofmannEtAl2007,
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.},
added-at = {2016-06-12T17:03:13.000+0200},
address = {Cambridge, MA},
biburl = {https://www.bibsonomy.org/bibtex/2bae484a74409e5962d6c9748e4f155a8/flint63},
editor = {Bakir, G\"{o}khan H. and Hofmann, Thomas and Sch\"{o}lkopf, Bernhard and Smola, Alexander J. and Taskar, Ben and Vishwanathan, S. V. N.},
file = {eBook:2007/BakirHofmannEtAl2007.pdf:PDF;MIT Press Product Page:http\://mitpress.mit.edu/books/predicting-structured-data:URL;Amazon Search inside:http\://www.amazon.de/gp/reader/0262026171/:URL},
groups = {public},
interhash = {e411df554b2106dd3c3a9d17bcb9406b},
intrahash = {bae484a74409e5962d6c9748e4f155a8},
isbn = {978-0-262-02617-8},
keywords = {01801 105 book shelf mitpress ai learn knowledge information processing},
publisher = {MIT Press},
series = {Neural Information Processing},
timestamp = {2018-04-28T20:45:17.000+0200},
title = {Predicting Structured Data},
username = {flint63},
year = 2007
}