Matching Pursuit algorithms learn a function that is a weighted sum of basis functions, by sequentially appending functions to an initially empty basis, to approximate a target fu...
In this paper, we propose a probabilistic kernel approach to preference learning based on Gaussian processes. A new likelihood function is proposed to capture the preference relat...
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 propose in this paper a general kernel framework to deal with database object retrieval embedded in images with heterogeneous background. We use local features computed on fuzz...
Philippe Henri Gosselin, Matthieu Cord, Sylvie Phi...
Naive Bayes Nearest Neighbor (NBNN) has recently been proposed as a powerful, non-parametric approach for object classification, that manages to achieve remarkably good results t...
Tinne Tuytelaars, Mario Fritz, Kate Saenko, Trevor...