This paper introduces a new approach to actionvalue function approximation by learning basis functions from a spectral decomposition of the state-action manifold. This paper exten...
Abstract. In this study we propose a novel model for the representation of biological networks and provide algorithms for learning model parameters from experimental data. Our appr...
Background: Hidden Markov Models (HMMs) provide an excellent means for structure identification and feature extraction on stochastic sequential data. An HMM-with-Duration (HMMwD) ...
In this work we aim to capitalize on the availability of Internet image search engines to automatically create image training sets from user provided queries. This problem is part...
Many multimedia applications rely on the computation of logarithms, for example, when estimating log-likelihoods for Gaussian Mixture Models. Knowing of the demand to compute loga...