This paper describes a clustering algorithm for vector quantizers using a "stochastic association model". It offers a new simple and powerful softmax adaptation rule. Th...
In this paper we propose a genetic programming approach to learning stochastic models with unsymmetrical noise distributions. Most learning algorithms try to learn from noisy data...
In this paper we survey some well-known approaches proposed as general models for calculi dealing with names (like for example process calculi with name-passing). We focus on (pre)...
Abstract. The paper is devoted to characterizing systems with random behaviours. The characterization is based on considering systems in terms of their possible runs, called proces...
We introduce CoCasl as a light-weight but expressive coalgebraic extension of the algebraic specification language Casl. CoCasl allows the nested combination of algebraic datatype...