We describe a neurally-inspired, unsupervised learning algorithm that builds a non-linear generative model for pairs of face images from the same individual. Individuals are then ...
Abstract-- A main difficulty that arises in the context of probabilistic localization is the design of an appropriate observation model, i.e., determining the likelihood of a senso...
Maren Bennewitz, Cyrill Stachniss, Sven Behnke, Wo...
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...
This article deals with the identification of gene regulatory networks from experimental data using a statistical machine learning approach. A stochastic model of gene interactio...
As data mining techniques are being increasingly applied to non-traditional domains, existing approaches for finding frequent itemsets cannot be used as they cannot model the req...