We consider an optimization problem in probabilistic inference: Given n hypotheses Hj, m possible observations Ok, their conditional probabilities pk j, and a particular Ok, selec...
An important focus of recent CBR research is on how to develop strategies for achieving compact, competent case-bases, as a way to improve the performance of CBR systems. However, ...
PANIC (Parallelism And Neural networks In Classifier systems) is a parallel system to evolve behavioral strategies codified by sets of rules. It integrates several adaptive techni...
We present a new ensemble method that uses Entropy Guided Transformation Learning (ETL) as the base learner. The proposed approach, ETL Committee, combines the main ideas of Baggin...
Recommender systems are widely used to cope with the problem of information overload and, consequently, many recommendation methods have been developed. However, no one technique i...