In this paper, we adopt general-sum stochastic games as a framework for multiagent reinforcement learning. Our work extends previous work by Littman on zero-sum stochastic games t...
In the past years, the theory and practice of machine learning and data mining have been focused on static and finite data sets from where learning algorithms generate a static m...
— Incremental rule base learning techniques can be used to learn models and classifiers from interval or fuzzyvalued data. These algorithms are efficient when the observation e...
An efficient framework is proposed for the fast recovery of Bayesian network classifier. A novel algorithm, called Iterative Parent-Child learningBayesian Network Classifier (IPC-...
— In this paper, we present a novel approach to partitioning pattern spaces using a multiobjective genetic algorithm for identifying (near-)optimal subspaces for hierarchical lea...