This paper presents an investigation into the classification of a difficult data set containing large intra-class variability but low inter-class variability. Standard classifiers...
This paper proposes a novel approach for rank level fusion which gives improved performance gain verified by experimental results. In the absence of ranked features and instead of...
Abstract. This paper introduces Deft, a new multitask learning approach for rule learning algorithms. Like other multitask learning systems, the one proposed here is able to improv...
—This paper evaluates techniques for improving operating system and network protocol software support for high-performance World Wide Web servers. We study approaches in three ca...
Erich M. Nahum, Tsipora P. Barzilai, Dilip D. Kand...
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...