Adaptive Mixtures of Probabilistic Transducers

We describe and analyze a mixture model for supervised learning of probabilistic transducers. We devise an on-line learning algorithm that eeciently 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 innnite) pool of models. We also present an application of the model for inducing a noun phrase recognizer.