We derive PAC-Bayesian generalization bounds for supervised and unsupervised learning models based on clustering, such as co-clustering, matrix tri-factorization, graphical models...
Methods based on 1-relaxation, such as basis pursuit and the Lasso, are very popular for sparse regression in high dimensions. The conditions for success of these methods are now ...
We study losses for binary classification and class probability estimation and extend the understanding of them from margin losses to general composite losses which are the compos...
Subgroup discovery aims at finding subsets of a population whose class distribution is significantly different from the overall distribution. A number of multi-class subgroup disc...
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...