We consider the Bellman residual minimization approach for solving discounted Markov decision problems, where we assume that a generative model of the dynamics and rewards is avai...
We present a general approach to model selection and regularization that exploits unlabeled data to adaptively control hypothesis complexity in supervised learning tasks. The idea ...
Background: Accurate sequence alignments are essential for homology searches and for building three-dimensional structural models of proteins. Since structure is better conserved ...
Model-based segmentation approaches, such as those employing Active Shape Models (ASMs), have proved to be useful for medical image segmentation and understanding. To build the mo...
Relations between models are important for effective automatic validation, for comparing implementations with specifications, and for increased understanding of embedded systems d...