Machine Learning - Special issue on learning with probabilistic representations
An adaptive prequential learning framework for bayesian network classifiers
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
Bias management of bayesian network classifiers
DS'05 Proceedings of the 8th international conference on Discovery Science
A conditional independence algorithm for learning undirected graphical models
Journal of Computer and System Sciences
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This thesis is concerned with adaptive learning algorithms for Bayesian network classifiers (BNCs) in a prequential (on-line) learning scenario capable of handling the cost-performance trade-off and concept drift. All these algorithms are integrated into the adaptive prequential framework for supervised learning, AdPreqFr4SL. We evaluated our algorithms on artificial domains and benchmark problems and show their advantages and future applicability in real-world on-line learning systems.