Generalization Performances of Perceptrons
ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
Generalization Performance of Classifiers in Terms of Observed Covering Numbers
EuroCOLT '99 Proceedings of the 4th European Conference on Computational Learning Theory
Boosting in the presence of noise
Journal of Computer and System Sciences - Special issue: Learning theory 2003
A Probabilistic Approach for Mining Drifting User Interest
APWeb/WAIM '09 Proceedings of the Joint International Conferences on Advances in Data and Web Management
The complexity of theory revision
Artificial Intelligence
PAC-Learning with general class noise models
KI'12 Proceedings of the 35th Annual German conference on Advances in Artificial Intelligence
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A model of machine learning in which the concept to be learned may exhibit uncertain or probabilistic behavior is investigated. Such probabilistic concepts (or p-concepts) may arise in situations such as weather prediction, where the measured variables and their accuracy are insufficient to determine the outcome with certainty. It is required that learning algorithms be both efficient and general in the sense that they perform well for a wide class of p-concepts and for any distribution over the domain. Many efficient algorithms for learning natural classes of p-concepts are given, and an underlying theory of learning p-concepts is developed in detail.