Semi-naive Bayesian classifier
EWSL-91 Proceedings of the European working session on learning on Machine learning
Learning and Revising User Profiles: The Identification ofInteresting Web Sites
Machine Learning - Special issue on multistrategy learning
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Adaptive Probabilistic Networks with Hidden Variables
Machine Learning - Special issue on learning with probabilistic representations
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
Machine Learning
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Machine Learning for User Modeling
User Modeling and User-Adapted Interaction
Induction of Recursive Bayesian Classifiers
ECML '93 Proceedings of the European Conference on Machine Learning
Adaptive bayes for a student modeling prediction task based on learning styles
UM'03 Proceedings of the 9th international conference on User modeling
Probabilistic graphical models in artificial intelligence
Applied Soft Computing
Incremental discretization for Naïve-Bayes classifier
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
A comparative performance study of feature selection methods for the anti-spam filtering domain
ICDM'06 Proceedings of the 6th Industrial Conference on Data Mining conference on Advances in Data Mining: applications in Medicine, Web Mining, Marketing, Image and Signal Mining
ECCBR'06 Proceedings of the 8th European conference on Advances in Case-Based Reasoning
Stream-based event prediction using bayesian and bloom filters
Proceedings of the 4th ACM/SPEC International Conference on Performance Engineering
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Several researchers have studied the application of Machine Learning techniques to the task of user modeling. As most of them pointed out, this task requires learning algorithms that should work on-line, incorporate new information incrementality, and should exhibit the capacity to deal with concept-drift. In this paper we present Adaptive Bayes, an extension to the well-known naive-Bayes, one of the most common used learning algorithms for the task of user modeling. Adaptive Bayes is an incremental learning algorithm that could work on-line. We have evaluated Adaptive Bayes on both frameworks. Using a set of benchmark problems from the UCI repository [2], and using several evaluation statistics, all the adaptive systems show significant advantages in comparison against their non-adaptive versions.