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12th IEEE Symposium on Computer-Based Medical Systems (CBMS'99)
Asynchronous, Parallel Pseudo-Gibbs Classification of the VF Dataset
Stamford, Connecticut
June 18-June 20
ISBN: 0-7695-0234-2
T. Dagget, Computer Sciences Corporation
I.R. Greenshields, University of Connecticut
G. Weerasinghe, University of Connecticut
The cryosectioned Visible Female dataset is a massive dataset spanning the length of the body in 0.33mm slices. It is infeasible to compute globally over this dataset. However, even when local computations are considered, the dataset is large enough to merit partitioning. In the case of Gibbs classification, such partitioning is inimical to the goal of Gibbs classification. In this paper we discuss a parameterized pseudo-Gibbsian approach to classifying the VF dataset which is stronger than ICM but weaker than a full Gibbs classification. We show how it is implemented in terms of asynchronous MPI.
Citation:
T. Dagget, I.R. Greenshields, G. Weerasinghe, "Asynchronous, Parallel Pseudo-Gibbs Classification of the VF Dataset," cbms, pp.164, 12th IEEE Symposium on Computer-Based Medical Systems (CBMS'99), 1999
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