In this work we take a novel view of nonlinear manifold learning. Usually, manifold learning is formulated in terms of finding an embedding or `unrolling' of a manifold into ...
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...
The Defense Applied Research Projects Agency (DARPA) Learning Applied to Ground Vehicles (LAGR) program aims to develop algorithms for autonomous vehicle navigation that learn how...
James S. Albus, Roger Bostelman, Tommy Chang, Tsai...
The computational complexity of current visual categorization algorithms scales linearly at best with the number of categories. The goal of classifying simultaneously Ncat = 104 -...
A key difficulty in the maintenance and evolution of complex software systems is to recognize and understand the implicit dependencies that define contracts that must be respecte...