The development of the XCS Learning Classifier System has produced a robust and stable implementation that performs competitively in direct-reward environments. Although investig...
The paper addresses the question whether it is possible for a machine to learn to distinguish and recognise famous musicians (concert pianists), based on their style of playing. We...
: This paper addresses the inference of probabilistic classification models using weakly supervised learning. The main contribution of this work is the development of learning meth...
We introduce the posterior probabilistic clustering (PPC), which provides a rigorous posterior probability interpretation for Nonnegative Matrix Factorization (NMF) and removes th...
In this paper we argue that maximum expected utility is a suitable framework for modeling a broad range of decision problems arising in pattern recognition and related fields. Exa...