This paper introduces a new problem for which machine-learning tools may make an impact. The problem considered is termed "compressive sensing", in which a real signal o...
We propose a novel approach to experience collection for autonomous service robots performing complex activities. This approach enables robots to data for many learning problems at...
In this paper, we propose a novel transductive learning framework named manifold-ranking based image retrieval (MRBIR). Given a query image, MRBIR first makes use of a manifold ra...
The sliding window approach of detecting rigid objects (such as cars) is predicated on the belief that the object can be identified from the appearance in a small region around the...
Multiple-instance learning (MIL) is a generalization of the supervised learning problem where each training observation is a labeled bag of unlabeled instances. Several supervised ...