In many real-world domains, undirected graphical models such as Markov random fields provide a more natural representation of the dependency structure than directed graphical mode...
Sushmita Roy, Terran Lane, Margaret Werner-Washbur...
Abstract. We propose an online learning algorithm to tackle the problem of learning under limited computational resources in a teacher-student scenario, over multiple visual cues. ...
Machine learning systems offer unparalled flexibility in dealing with evolving input in a variety of applications, such as intrusion detection systems and spam e-mail filtering. H...
Marco Barreno, Blaine Nelson, Russell Sears, Antho...
learning (EBL) component. In this paper we provide a brief review of FOIL and FOCL, then discuss how operationalizing a domain theory can adversely affect the accuracy of a learned...
We propose a new local learning scheme that is based on the principle of decisiveness: the learned classifier is expected to exhibit large variability in the direction of the test ...