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...
Abstract. Stochastic deterministic finite automata have been introduced and are used in a variety of settings. We report here a number of results concerning the learnability of th...
This paper describes the conversion of a Hidden Markov Model into a sequential transducer that closely approximates the behavior of the stochastic model. This transformation is es...
Internet search companies sell advertisement slots based on users’ search queries via an auction. Advertisers have to solve a complex optimization problem of how to place bids o...
Stochastic analysis techniques for real-time systems model the execution time of tasks as random variables. These techniques constitute a very powerful tool to study the behaviour...