Previous algorithms for learning lexicographic preference models (LPMs) produce a "best guess" LPM that is consistent with the observations. Our approach is more democra...
Fusun Yaman, Thomas J. Walsh, Michael L. Littman, ...
Class binarizations are effective methods for improving weak learners by decomposing multi-class problems into several two-class problems. This paper analyzes how these methods can...
: Recently, multi-instance classification algorithm BP-MIP and multi-instance regression algorithm BP-MIR both based on neural networks have been proposed. In this paper, neural ne...
In this paper we demonstrate how weighted majority voting with multiplicative weight updating can be applied to obtain robust algorithms for learning binary relations. We first pre...
For complex database prediction problems such as medical diagnosis, the ARTMAP-IC neural network adds distributed prediction and category instance counting to the basic fuzzy ARTM...