Previous discretization techniques have discretized numeric attributes into disjoint intervals. We argue that this is neither necessary nor appropriate for naive-Bayes classifiers...
In this paper, we examine a method for feature subset selection based on Information Theory. Initially, a framework for de ning the theoretically optimal, but computationally intr...
The success ofreinforcement learninginpractical problems depends on the ability to combine function approximation with temporal di erence methods such as value iteration. Experime...
We present a browser extension to dynamically learn to filter unwanted images (such as advertisements or flashy graphics) based on minimal user feedback. To do so, we apply the we...
We present a sub-symbolic computational model for effecting knowledge re-representation and insight. Given a set of data, manifold learning is used to automatically organize the d...