Recent work in supervised learning has shown that a surprisingly simple Bayesian classifier with strong assumptions of independence among features, called naive Bayes, is competit...
While several powerful domain-independent planners have recently been developed, no one of these clearly outperforms all the others in every known benchmark domain. We present PbP...
Alfonso Gerevini, Alessandro Saetti, Mauro Vallati
Knowledge transfer is computationally challenging, due in part to the curse of dimensionality, compounded by source and target domains expressed using different features (e.g., do...
Hashing, which tries to learn similarity-preserving binary codes for data representation, has been widely used for efficient nearest neighbor search in massive databases due to i...
As the accuracy of conventional classifiers, based only on a static partitioning of feature space, appears to be approaching a limit, it may be useful to consider alternative appro...