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Long viewed as a strong statistical inference technique, Bayesian networks have emerged as an important class of applications for high-performance computing. We have applied an architecture-conscious approach to parallelizing the Lauritzen-Spiegelhalter Junction Tree algorithm for exact inferencing of Bayesian networks. In optimizing the Junction Tree(More)
A significant problem associated with application of the Back Propagation learning paradigm for pattern classification is the lack of high accuracy in generalization when the domain is large. In this paper we describe a multiple neural network system, which uses two self-organizing neural networks that work as teaching data filters (feature extractors),(More)
Here we demonstrate that separation of proteolytic peptides, having the same net charge and one basic residue, is affected by their specific orientation toward the stationary phase in ion-exchange chromatography. In electrostatic repulsion-hydrophilic interaction chromatography (ERLIC) with an anion-exchange material, the C-terminus of the peptides is, on(More)
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