We introduce a new model for learning in the presence of noise, which we call the Nasty Noise model. This model generalizes previously considered models of learning with noise. Th...
The widespread use of ontologies to associate semantics with data has resulted in a growing interest in the problem of learning predictive models from data sources that use differe...
In this paper we analyze the PAC learning abilities of several simple iterative algorithms for learning linear threshold functions, obtaining both positive and negative results. W...
Learning from positive examples occurs very frequently in natural learning. The PAC learning model of Valiant takes many features of natural learning into account, but in most case...
We describe a new boosting algorithm which generates only smooth distributions which do not assign too much weight to any single example. We show that this new boosting algorithm ...