Adaptive Mixtures of Probabilistic Transducers

Yoram Singer

Research output: Contribution to journalArticle

9 Scopus citations

Abstract

We describe and analyze a mixture model for supervised learning of probabilistic transducers. We devise an online learning algorithm that efficiently infers the structure and estimates the parameters of each probabilistic transducer in the mixture. Theoretical analysis and comparative simulations indicate that the learning algorithm tracks the best transducer from an arbitrarily large (possibly infinite) pool of models. We also present an application of the model for inducing a noun phrase recognizer.

Original languageEnglish (US)
Pages (from-to)1711-1733
Number of pages23
JournalNeural computation
Volume9
Issue number8
DOIs
StatePublished - Nov 15 1997

All Science Journal Classification (ASJC) codes

  • Arts and Humanities (miscellaneous)
  • Cognitive Neuroscience

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