This paper introduces a new algorithm, Q2, foroptimizingthe expected output ofamultiinput noisy continuous function. Q2 is designed to need only a few experiments, it avoids stron...
Andrew W. Moore, Jeff G. Schneider, Justin A. Boya...
Supervised learning deals with the inference of a distribution over an output or label space $\CY$ conditioned on points in an observation space $\CX$, given a training dataset $D$...
We apply an adapted version of Particle Swarm Optimization to distributed unsupervised robotic learning in groups of robots with only local information. The performance of the lea...
Supervised learning deals with the inference of a distribution over an output or label space Y conditioned on points in an observation space X , given a training dataset D of pair...
: 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