Learning and inference over constrained output

Vasin Punyakanok, Dan Roth, Wen Tau Yih, Dav Zimak

Research output: Contribution to journalConference articlepeer-review

Abstract

We study learning structured output in a discriminative framework where values of the output variables are estimated by local classifiers. In this framework, complex dependencies among the output variables are captured by constraints and dictate which global labels can be inferred. We compare two strategies, learning independent classifiers and inference based training , by observing their behaviors in different conditions. Experiments and theoretical justification lead to the conclusion that using inference based learning is superior when the local classifiers are difficult to learn but may require many examples before any discernible difference can be observed.

Original languageEnglish (US)
Pages (from-to)1124-1129
Number of pages6
JournalIJCAI International Joint Conference on Artificial Intelligence
StatePublished - Dec 1 2005
Event19th International Joint Conference on Artificial Intelligence, IJCAI 2005 - Edinburgh, United Kingdom
Duration: Jul 30 2005Aug 5 2005

ASJC Scopus subject areas

  • Artificial Intelligence

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