Giuseppe Lo Bello

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In this paper an extensive experimental evaluation of an evolutionary approach t o c o n-cept learning is presented. The experimentation , performed with the system G-NET, investigates the eeectiveness of the approach along the following dimensions: Robustness with respect to parameter setting, eeective-ness of the MDL criterion coupled with a stochastic(More)
This paper presents a highly parallel genetic algorithm , designed for concept induction in proposi-tional and first order logics. The system exploits niches and species for learning multimodal concepts ; it deeply differs from other systems because of the distributed architecture, which totally eliminates the concept of common memory. A first(More)
The automatic construction of classiiers (programs able to correctly classify data collected from the real world) is one of the major problems in pattern recognition and in a wide area related to Artiicial Intelligence, including Data Mining. In this paper we present G-Net, a distributed algorithm able to infer classiiers from pre-collected data, and its(More)
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