Abstracts "Mixtures at the Interface" David Scott, Rice University Mixture modeling provides an effective framework for complex, high-dimensional data. The potential of m...
A statistical generative model is presented as an alternative to negative selection in anomaly detection of string data. We extend the probabilistic approach to binary classificat...
We present a system for model-based source separation for use on single channel speech mixtures where the precise source characteristics are not known a priori. The sources are mo...
A solution to the problem of homograph (words with multiple distinct meanings) identification is proposed and evaluated in this paper. It is demonstrated that a mixture model base...
Motivated by the poor performance (linear complexity) of the EM algorithm in clustering large data sets, and inspired by the successful accelerated versions of related algorithms l...