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...
Appropriate selection of the kernel function, which implicitly defines the feature space of an algorithm, has a crucial role in the success of kernel methods. In this paper, we co...
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 propose a second order optimization method to learn models where both the dimensionality of the parameter space and the number of training samples is high. In ou...
Recent advances in large-margin classification of data residing in general metric spaces (rather than Hilbert spaces) enable classification under various natural metrics, such as ...
Lee-Ad Gottlieb, Leonid Kontorovich, Robert Krauth...