We develop kernels for measuring the similarity between relational instances using background knowledge expressed in first-order logic. The method allows us to bridge the gap betw...
In this paper, we continue our study of learning an optimal kernel in a prescribed convex set of kernels, [18]. We present a reformulation of this problem within a feature space e...
— In this paper, we show that one-class SVMs can also utilize data covariance in a robust manner to improve performance. Furthermore, by constraining the desired kernel function ...
We introduce an algorithm that, given n objects, learns a similarity matrix over all n2 pairs, from crowdsourced data alone. The algorithm samples responses to adaptively chosen t...
Omer Tamuz, Ce Liu, Serge Belongie, Ohad Shamir, A...
This paper1 presents novel algorithms and applications for a particular class of mixed-norm regularization based Multiple Kernel Learning (MKL) formulations. The formulations assu...
Jonathan Aflalo, Aharon Ben-Tal, Chiranjib Bhattac...