The high computational cost of nonlinear support vector machines has limited their usability for large-scale problems. We propose two novel stochastic algorithms to tackle this pr...
Transductive inference on graphs such as label propagation algorithms is receiving a lot of attention. In this paper, we address a label propagation problem on multiple networks a...
We study a class of algorithms that speed up the training process of support vector machines (SVMs) by returning an approximate SVM. We focus on algorithms that reduce the size of...
In this paper, we study the behavior of collaborative filtering based recommendations under evolving user profile scenarios. We propose a systematic validation methodology that ...
Olfa Nasraoui, Jeff Cerwinske, Carlos Rojas, Fabio...
In this paper we propose a method for grouping and summarizing large sets of association rules according to the items contained in each rule. We use hierarchical clustering to par...