Parallelizing Support Vector Machines
EMSL Project ID
35795
Abstract
The proposed project will focus on distributed and shared memory techniques to improve the execution efficiency and accuracy of Support Vector Machines (SVM). Current research suggests that SVM will play a role in classifying genomes and proteomes [1-4] and other fields of interest to EMSL, PNNL and DOE however, these techniques are computationally expensive, which limits their usefulness for large-scale problems such as community-level proteomic analysis. To address this problem, we are investigating parallelization techniques to take advantage of distributed and shared-memory computers.
Project Details
Start Date
2009-06-19
End Date
2010-06-20
Status
Closed
Released Data Link
Team
Principal Investigator
Team Members