The extraction of optimal features, in a classification sense, is still quite challenging in the context of large-scale classification problems (such as visual recognition), inv...
The success of simple methods for classification shows that is is often not necessary to model complex attribute interactions to obtain good classification accuracy on practical p...
Albert Bifet, Eibe Frank, Geoffrey Holmes, Bernhar...
In this paper, we compare both discriminative and generative parameter learning on both discriminatively and generatively structured Bayesian network classifiers. We use either ma...
Literature on the use of machine learning (ML) algorithms for classifying IP traffic has relied on fullflows or the first few packets of flows. In contrast, many real-world scenar...
We have previously introduced the Learn++ algorithm that provides surprisingly promising performance for incremental learning as well as data fusion applications. In this contribut...
Michael Muhlbaier, Apostolos Topalis, Robi Polikar