We examine the set covering machine when it uses data-dependent half-spaces for its set of features and bound its generalization error in terms of the number of training errors an...
Mario Marchand, Mohak Shah, John Shawe-Taylor, Mar...
Abstract. We investigate the generalization behavior of sequential prediction (online) algorithms, when data are generated from a probability distribution. Using some newly develop...
Machine learning research often has a large experimental component. While the experimental methodology employed in machine learning has improved much over the years, repeatability ...
Background: The application of machine learning to classification problems that depend only on positive examples is gaining attention in the computational biology community. We an...
Malik Yousef, Segun Jung, Louise C. Showe, Michael...
We propose a theoretical framework for specification and analysis of a class of learning problems that arise in open-ended environments that contain multiple, distributed, dynamic...