Supervised learning is difficult with high dimensional input spaces and very small training sets, but accurate classification may be possible if the data lie on a low-dimensional ...
We present a method for parameter learning in relational Bayesian networks (RBNs). Our approach consists of compiling the RBN model into a computation graph for the likelihood fun...
We address the problem of detecting batches of emails that have been created according to the same template. This problem is motivated by the desire to filter spam more effectivel...
This paper proposes a new map building framework for mobile robot named Localization-Free Mapping by Dimensionality Reduction (LFMDR). In this framework, the robot map building is...
In this paper we consider the problem of classification in the presence of pairwise constraints, which consist of pairs of examples as well as a binary variable indicating whether...