Model compensation schemes are a powerful approach to handling mismatches between training and testing conditions. Normally these schemes are run in a batch adaptation mode, re-re...
— Many architectures have been proposed to solve tightly-coupled multirobot tasks (MT) through coalitions of heterogeneous robots. However, several issues remain unaddressed. As ...
This paper proposes a general boosting framework for combining multiple kernel models in the context of both classification and regression problems. Our main approach is built on...
Considerable advances have been made in learning to recognize and localize visual object classes. Simple bag-offeature approaches label each pixel or patch independently. More adv...
Uncertainty always exists as an unavoidable factor in any pervasive context-aware applications. This is mostly caused by the imperfectness and incompleteness of data. In this pape...