Relational Markov models (RMMs) are a generalization of Markov models where states can be of different types, with each type described by a different set of variables. The domain ...
In spite of the popularity of probabilistic mixture models for latent structure discovery from data, mixture models do not have a natural mechanism for handling sparsity, where ea...
tn this paper, we present a new 1)hrase break prediction architecture that integrates probabilistic apt)roach with decision-tree based error correction. The probabilistic method a...
Approximate Bayesian Gaussian process (GP) classification techniques are powerful nonparametric learning methods, similar in appearance and performance to support vector machines....
In this paper, we present a novel method for model estimation for visual servoing. This method employs a particle filter algorithm to estimate the depth of the image features onli...