This paper presents and evaluates sequential instance-based learning (SIBL), an approach to action selection based upon data gleaned from prior problem solving experiences. SIBL le...
Abstract. We propose a framework that learns functional objectes from spatio-temporal data sets such as those abstracted from video. The data is represented as one activity graph t...
Muralikrishna Sridhar, Anthony G. Cohn, David C. H...
Probabilistic feature relevance learning (PFRL) is an effective method for adaptively computing local feature relevance in content-based image retrieval. It computes flexible retr...
The human ability to learn difficult object categories from just a few views is often explained by an extensive use of knowledge from related classes. In this work we study the use...
This research aims at studying the effects of exchanging information during the learning process in Multiagent Systems. The concept of advice-exchange, introduced in (Nunes and Ol...