Abstract. Many large-scale optimization problems rely on graph theoretic solutions; yet high-performance computing has traditionally focused on regular applications with high degre...
Feature selection is an important task in order to achieve better generalizability in high dimensional learning, and structure learning of Markov random fields (MRFs) can automat...
This paper takes an economic approach to derive an evolutionary learning model based entirely on the endogenous employment of genetic operators in the service of self-interested a...
Recent local state-of-the-art stereo algorithms based on variable cost aggregation strategies allow for inferring disparity maps comparable to those yielded by algorithms based on ...
Stefano Mattoccia, Simone Giardino, Andrea Gambini
This paper presents a novel approach to clustering using an accuracy-based Learning Classifier System. Our approach achieves this by exploiting the generalization mechanisms inher...