In this paper, we address the problem of semisupervision in the framework of parametric clustering by using labeled and unlabeled data together. Clustering algorithms can take adv...
From working with location-based information systems we know that positioning is problematic. A different approach was tested, where users themselves were allowed to name and defi...
We take a dual view of Markov processes ? advocated by Kozen ? as transformers of bounded measurable functions. We redevelop the theory of labelled Markov processes from this view ...
Philippe Chaput, Vincent Danos, Prakash Panangaden...
Virtually all existing classification techniques label one sample at a time. In this paper, we highlight the potential benefits of group based classification (GBC), where the clas...
—This paper proposes the sequential context inference (SCI) algorithm for Markov random field (MRF) image analysis. This algorithm is designed primarily for fast inference on an...