Imbalanced class problems appear in many real applications of classification learning. We propose a novel sampling method to improve bagging for data sets with skewed class distri...
Cross validation allows models to be tested using the full training set by means of repeated resampling; thus, maximizing the total number of points used for testing and potential...
We present a theoretical study on the discriminative clustering framework, recently proposed for simultaneous subspace selection via linear discriminant analysis (LDA) and cluster...
Our goal is to define a list of tasks for graph visualization that has enough detail and specificity to be useful to designers who want to improve their system and to evaluators w...
Current research in the field of automatic plagiarism detection for text documents focuses on algorithms that compare plagiarized documents against potential original documents. Th...