Relational world models that can be learned from experience in stochastic domains have received significant attention recently. However, efficient planning using these models rema...
This paper is concerned with the problem of predicting relative performance of classification algorithms. It focusses on methods that use results on small samples and discusses th...
In many prediction tasks, selecting relevant features is essential for achieving good generalization performance. Most feature selection algorithms consider all features to be a p...
Su-In Lee, Vassil Chatalbashev, David Vickrey, Dap...
Many learning algorithms form concept descriptions composed of clauses, each of which covers some proportion of the positive training data and a small to zero proportion of the ne...
In this paper, an easily implemented semi-supervised graph learning method is presented for dimensionality reduction and clustering, using the most of prior knowledge from limited...