Monthly
288 pp. per issue
6 x 9, illustrated
ISSN
0899-7667
E-ISSN
1530-888X
2014 Impact factor:
2.21

Neural Computation

November 15, 1997, Vol. 9, No. 8, Pages 1711-1733.
(doi: 10.1162/neco.1997.9.8.1711)
© 1997 Massachusetts Institute of Technology
Adaptive Mixtures of Probabilistic Transducers
Article PDF (184.46 KB)
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.