We study the empirical meaning of randomness with respect to a family of probability distributions P, where is a real parameter, using algorithmic randomness theory. In the case w...
We consider the problem of aggregation for uncertain and imprecise data. For such data, we define aggregation operators and use them to provide information on properties and patte...
This work proposes an approach to generate weighted random patterns which can maximally excite a circuit during its burn-in testing. The approach is based on a probability model a...
ÐThis paper describes a novel framework for comparing and matching corrupted relational graphs. The paper develops the idea of edit-distance originally introduced for graph-matchi...
Richard Myers, Richard C. Wilson, Edwin R. Hancock
We present a velocity-constrained front propagation approach for myocardium segmentation from magnetic resonance intensity image (MRI) and its matching phase contrast velocity (PC...
Computing and storing probabilities is a hard problem as soon as one has to deal with complex distributions over multiples random variables. The problem of efficient representati...
Abstract. Most of the work in machine learning assume that examples are generated at random according to some stationary probability distribution. In this work we study the problem...
We propose a novel replacement algorithm, called InterReference Gap Distribution Replacement (IGDR), for setassociative secondary caches of processors. IGDR attaches a weight to e...
We propose a simple and intuitive cost mechanism which assigns costs for the competitive usage of m resources by n selfish agents. Each agent has an individual demand; demands are...
Marios Mavronicolas, Panagiota N. Panagopoulou, Pa...
Estimation of distribution algorithms (EDAs) try to solve an optimization problem by finding a probability distribution focussed around its optima. For this purpose they conduct ...