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High-performance Support Vector Machines for Data-Intensive Applications


EMSL Project ID
29393

Abstract

Support vector machines (SVM) are a software technology with broad applicability for creating binary classifiers using training data. In much the same way that SCALAPACK efficiently enables many application areas that rely heavily on linear algebra, we are applying SVM's to perform large-scale binary classification in problem spaces such as remote protein homology and peptide identification. Through the proposed work we will profile a collection of publically available and home-grown SVM implementaitons focusing on efficiency of hardware utilization, time to solution, and statistical quality of solutions.

Project Details

Project type
Limited Scope
Start Date
2008-01-31
End Date
2008-03-04
Status
Closed

Team

Principal Investigator

Christopher Oehmen
Institution
Pacific Northwest National Laboratory

Team Members

Bobbie-Jo Webb-Robertson
Institution
Pacific Northwest National Laboratory

Related Publications

Oehmen, CS and BM Webb-Robertson (2008) Evaluating the Computational Requirements of using SVM software to train Data-Intensive Problems. Nova Science Publishers, New York, accepted.