Multi-label learning deals with data associated with multiple labels simultaneously. Previous work on multi-label learning assumes that for each instance, the "full" lab...
This paper investigates the application of randomized algorithms for large scale SVM learning. The key contribution of the paper is to show that, by using ideas random projections...
We study the problem of domain transfer for a supervised classification task in mRNA splicing. We consider a number of recent domain transfer methods from machine learning, includ...
Gabriele Schweikert, Christian Widmer, Bernhard Sc...
Abstract. Factored Markov Decision Processes is the theoretical framework underlying multi-step Learning Classifier Systems research. This framework is mostly used in the context ...
This paper presents an approach for classifying students in order to predict their final grade based on features extracted from logged data in an education web-based system. A comb...