This paper presents a method of generating Mercer Kernels from an ensemble of probabilistic mixture models, where each mixture model is generated from a Bayesian mixture density e...
A major obstacle to the construction of a probabilistic translation model is the lack of large parallel corpora. In this paper we first describe a parallel text mining system that...
The paper reports on an experiment to provide the Erlang programming language with a tool package for convenient trace generation, collection and to support analysis of traces usi...
We present a probabilistic model for a document corpus that combines many of the desirable features of previous models. The model is called “GaP” for Gamma-Poisson, the distri...
We present a mixture model based approach for learning individualized behavior models for the Web users. We investigate the use of maximum entropy and Markov mixture models for ge...