Abstract-- Large graph datasets are common in many emerging database applications, and most notably in large-scale scientific applications. To fully exploit the wealth of informati...
We address instance-based learning from a perceptual organization standpoint and present methods for dimensionality estimation, manifold learning and function approximation. Under...
Abstract. This paper presents a method of solving initial value problems using Euler’s method, based on the domain of interval valued functions of a real variable. In contrast to...
POMDPs are a popular framework for representing decision making problems that contain uncertainty. The high computational complexity of finding exact solutions to POMDPs has spaw...
This paper analyses the Contrastive Divergence algorithm for learning statistical parameters. We relate the algorithm to the stochastic approximation literature. This enables us t...