The probabilistic concept formation general problem in dealing with mixed-data scale environments is due to the use of different evaluation function for each attribute type. We cl...
We claim and present arguments to the effect that a large class of manifold learning algorithms that are essentially local and can be framed as kernel learning algorithms will suf...
We consider the problem of structured classification, where the task is to predict a label y from an input x, and y has meaningful internal structure. Our framework includes super...
Peter L. Bartlett, Michael Collins, Benjamin Taska...
We propose a probabilistic, generative account of configural learning phenomena in classical conditioning. Configural learning experiments probe how animals discriminate and gener...
Aaron C. Courville, Nathaniel D. Daw, David S. Tou...
Based on a new framework for the description of N transparent motions we categorize different types of transparent-motion patterns. Confidence measures for the presence of all th...
Cicero Mota, Michael Dorr, Ingo Stuke, Erhardt Bar...