The nature of statistical learning theory
The nature of statistical learning theory
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Using MPI (2nd ed.): portable parallel programming with the message-passing interface
Using MPI (2nd ed.): portable parallel programming with the message-passing interface
Parallel data intensive computing in scientific and commercial applications
Parallel Computing - Parallel data-intensive algorithms and applications
LAPACK Working Note 94: A User''s Guide to the BLACS v1.0
LAPACK Working Note 94: A User''s Guide to the BLACS v1.0
A Proposal for a Set of Parallel Basic Linear Algebra Subprograms
A Proposal for a Set of Parallel Basic Linear Algebra Subprograms
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
A classification method based on generalized eigenvalue problems
Optimization Methods & Software - Systems Analysis, Optimization and Data Mining in Biomedicine
International Journal of Data Mining and Bioinformatics
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Classification is one of the most widely used methods in data mining, with numerous applications in biomedicine. The scope and the resolution of data involved in many real life applications require very efficient implementations of classification methods, developed to run on parallel or distributed computational systems. In this study we describe SVD-ReGEC, a fully parallel implementation, for distributed memory multicomputers, of a classification algorithm with a feature reduction. The classification is based on Regularized Generalized Eigenvalue Classifier (ReGEC) and the preprocessing stage is a filter method algorithm based on Singular Value Decomposition (SVD), that reduces the dimension of the space in which classification is accomplished. The implementation is tested on random datasets and results are discussed using standard parameters.