We present an efficient protocol for the privacy-preserving, distributed learning of decision-tree classifiers. Our protocol allows a user to construct a classifier on a database h...
In this paper we propose to combine two powerful ideas, boosting and manifold learning. On the one hand, we improve ADABOOST by incorporating knowledge on the structure of the dat...
: Image understanding often requires extensive background knowledge. The problem addressed in this paper is such knowledge can be acquired. We discuss how relational machine learni...