BSP-Based Support Vector Regression Machine Parallel Framework

DOI

10.2991/ijndc.2013.1.3.2How to use a DOI?

Keywords

parallel computing; bulk synchronous parallel; support vector regression machine (SVR); regression prediction.

Abstract

In this paper, we investigate the distributed parallel Support Vector Machine training strategy, and then propose a BSP-Based Support Vector Regression Machine Parallel Framework which can implement the most of distributed Support Vector Regression Machine algorithms. The major difference in these algorithms is the network topology among distributed nodes. Therefore, we adopt the Bulk Synchronous Parallel model to solve the strongly connected graph problem in exchanging support vectors among distributed nodes. In addition, we introduce the dynamic algorithms which can change the strongly connected graph among SVR distributed nodes in every BSP’s super-step. The performance of this framework has been analyzed and evaluated with KDD99 data and four DPSVR algorithms on the high-performance computer. The results prove that the framework can implement the most of distributed SVR algorithms and keep the performance of original algorithms.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Hong Zhang
AU  - Yongmei Lei
PY  - 2013
DA  - 2013/08/01
TI  - BSP-Based Support Vector Regression Machine Parallel Framework
JO  - International Journal of Networked and Distributed Computing
SP  - 134
EP  - 143
VL  - 1
IS  - 3
SN  - 2211-7946
UR  - https://doi.org/10.2991/ijndc.2013.1.3.2
DO  - 10.2991/ijndc.2013.1.3.2
ID  - Zhang2013
ER  -