This paper studies two types of spatial relationships that can be learned from training examples for object recognition. The first one employs deformable relationships between obj...
— This paper addresses the problem of closing the loop from perception to action selection for unmanned ground vehicles, with a focus on navigating slopes. A new non-parametric l...
Sisir Karumanchi, Thomas Allen, Tim Bailey, Steve ...
An approach to semi-supervised learning is proposed that is based on a Gaussian random field model. Labeled and unlabeled data are represented as vertices in a weighted graph, wit...
Autonomics or self-reorganization becomes pertinent for websites serving a large number of users with highly varying workloads. An important component of self-adaptation is to mod...
In this paper, we present a new rule induction algorithm for machine learning in medical diagnosis. Medical datasets, as many other real-world datasets, exhibit an imbalanced clas...