: The particle swarm is one of the most powerful methods for solving global optimization problems. This method is an adaptive algorithm based on social-psychological metaphor. A po...
Reza Rastegar, Mohammad Reza Meybodi, Kambiz Badie
A framework for task assignment in heterogeneous computing systems is presented in this work. The framework is based on a learning automata model. The proposed model can be used f...
One of the main problems in probabilistic grammatical inference consists in inferring a stochastic language, i.e. a probability distribution, in some class of probabilistic models...
We cast model-free reinforcement learning as the problem of maximizing the likelihood of a probabilistic mixture model via sampling, addressing both the infinite and finite horizo...
We propose and analyze a distribution learning algorithm for variable memory length Markov processes. These processes can be described by a subclass of probabilistic nite automata...