In this paper, we investigate a simple, mistakedriven learning algorithm for discriminative training of continuous density hidden Markov models (CD-HMMs). Most CD-HMMs for automat...
We introduce a robust probabilistic approach to modeling shape contours based on a lowdimensional, nonlinear latent variable model. In contrast to existing techniques that use obj...
We present a principled Bayesian framework for modeling partial memberships of data points to clusters. Unlike a standard mixture model which assumes that each data point belongs ...
Katherine A. Heller, Sinead Williamson, Zoubin Gha...
This paper addresses the problem of finding a small and coherent subset of points in a given data. This problem, sometimes referred to as one-class or set covering, requires to fi...
Stochastic topological models, and hidden Markov models in particular, are a useful tool for robotic navigation and planning. In previous work we have shown how weak odometric dat...