Learning Classifier Systems (LCSs), such as the accuracy-based XCS, evolve distributed problem solutions represented by a population of rules. During evolution, features are speci...
Martin V. Butz, Martin Pelikan, Xavier Llorà...
In this paper, we show that a Bio-inspired classifier’s accuracy can be dramatically improved if it operates on intelligent features. We propose a novel set of intelligent feat...
M. Zubair Shafiq, Syed Ali Khayam, Muddassar Faroo...
Over the last few years, social network systems have greatly increased users’ involvement in online content creation and annotation. Since such systems usually need to deal with...
Ivan Ivanov, Peter Vajda, Lutz Goldmann, Jong-Seok...
This paper describes an algorithm, called CQ-learning, which learns to adapt the state representation for multi-agent systems in order to coordinate with other agents. We propose ...
We propose a new unsupervised learning technique for extracting information about authors and topics from large text collections. We model documents as if they were generated by a...
Michal Rosen-Zvi, Chaitanya Chemudugunta, Thomas L...