Most of the search engine optimization techniques attempt to predict users interest by learning from the past information collected from different sources. But, a user's curr...
Sanasam Ranbir Singh, Hema A. Murthy, Timothy A. G...
We describe an algorithm for converting linear support vector machines and any other arbitrary hyperplane-based linear classifiers into a set of non-overlapping rules that, unlike...
When an optimization problem is encoded using genetic algorithms, one must address issues of population size, crossover and mutation operators and probabilities, stopping criteria...
A very undesirable behavior of any heuristic algorithm is to be stuck in some specific parts of the search space, in particular in the basins of attraction of the local optima. Wh...
Daniel Cosmin Porumbel, Jin-Kao Hao, Pascale Kuntz
— This paper presents an algorithm for adapting periodic behavior to gradual shifts in task parameters. Since learning optimal control in high dimensional domains is subject to t...