Abstract. The Bayesian approach to machine learning amounts to inferring posterior distributions of random variables from a probabilistic model of how the variables are related (th...
Deep learning has been successfully applied to perform non-linear embedding. In this paper, we present supervised embedding techniques that use a deep network to collapse classes....
Martin Renqiang Min, Laurens van der Maaten, Zinen...
Abstract--Kernel-based algorithms such as support vector machines have achieved considerable success in various problems in batch setting, where all of the training data is availab...
Jyrki Kivinen, Alex J. Smola, Robert C. Williamson
Qualitative models are often a useful abstraction of the physical world. Learning qualitative models from numerical data sible way to obtain such an abstraction. We present a new ...
Jure Zabkar, Martin Mozina, Ivan Bratko, Janez Dem...
Several published reports show that instancebased learning algorithms yield high classification accuracies and have low storage requirements during supervised learning application...