We present a subspace learning method, called Local Discriminant Embedding with Tensor representation (LDET), that addresses simultaneously the generalization and data representat...
Before applying learning algorithms to datasets, practitioners often globally discretize any numeric attributes. If the algorithm cannot handle numeric attributes directly, prior ...
In many practical applications, one is interested in generating a ranked list of items using information mined from continuous streams of data. For example, in the context of comp...
Delivery of content in networking and system administration curricula involves significant hands-on laboratory experience supplementing traditional classroom instruction at the Ro...
Abstract. This paper describes an experimental comparison between two standard supervised learning methods, namely Naive Bayes and Exemplar–basedclassification, on the Word Sens...