Concept drifting is an important and challenging research issue in the field of machine learning. This paper mainly addresses the issue of semantic concept drifting in time series...
We propose an unsupervised, probabilistic method for learning visual feature hierarchies. Starting from local, low-level features computed at interest point locations, the method c...
As a value flows across the boundary between interoperating languages, it must be checked and converted to fit the types and representations of the target language. For simple f...
Kathryn E. Gray, Robert Bruce Findler, Matthew Fla...
In this paper, we propose an iterative similarity propagation approach to explore the inter-relationships between Web images and their textual annotations for image retrieval. By ...
Abstract. In this paper we describe the application of a novel statistical videomodeling scheme to sequences of multiple sclerosis (MS) images taken over time. The analysis of the ...