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...
An important step in designing stochastic simulation is modeling the uncertainty in the input environment of the system being studied. Obtaining a reasonable representation of thi...
Abstract. The present paper presents a new approach of how to convert Gold-style [4] learning in the limit into stochastically finite learning with high confidence. We illustrate t...
In Data Oriented Parsing (DOP), an annotated corpus is used as a stochastic grammar. An input string is parsed by combining subtrees from the corpus. As a consequence, one parse t...