When a large amount of data are missing, or when multiple hidden nodes exist, learning parameters in Bayesian networks (BNs) becomes extremely difficult. This paper presents a lea...
A recent trend in exemplar based unsupervised learning is to formulate the learning problem as a convex optimization problem. Convexity is achieved by restricting the set of possi...
Abstract. Automata-based decision procedures commonly achieve optimal complexity bounds. However, in practice, they are often outperformed by sub-optimal (but more local-search bas...
This paper proposes two very fast graph theoretic heuristics for the low power binding problem given fixed number of resources and multiple architectures for the resources. First...
Support vector machines (SVMs) have played a key role in broad classes of problems arising in various fields. Much more recently, SVMs have become the tool of choice for problems...