In this paper, we present new approaches to handling drift and shift in on-line data streams with the help of evolving fuzzy systems (EFS), which are characterized by the fact tha...
In this paper we introduce the concept and method for adaptively tuning the model complexity in an online manner as more examples become available. Challenging classification pro...
Traditional machine-learned ranking algorithms for web search are trained in batch mode, which assume static relevance of documents for a given query. Although such a batch-learni...
This paper presents an innovative approach to personalize on-line content to the needs of individual learners. We use a regular educational environment, the BlackboardTM Learning ...
Guillermo Power, Hugh C. Davis, Alexandra I. Crist...
This paper was prepared for a lecture at a recent meeting of the German Chapter of ISKO devoted to Wissensorganisation mit Multimedialen Techniken [Knowledge Organization with Mul...