A game-theoretic approach for learning optimal parameter values for probabilistic rough set regions is presented. The parameters can be used to define approximation regions in a p...
Probabilistic (or randomized) decision trees can be used to compute Boolean functions. We consider two types of probabilistic decision trees - one has a certain probability to give...
Laura Mancinska, Maris Ozols, Ilze Dzelme-Berzina,...
Basis functions derived from an undirected graph connecting nearby samples from a Markov decision process (MDP) have proven useful for approximating value functions. The success o...
The rapidly growing web technologies and electronic commerce applications have stimulated the need of personalized and group decision support functionalities in eTourism intermedia...
The binary representation is widely used for representing focal sets of Dempster-Shafer belief functions because it allows to compute efficiently all relevant operations. However, ...