International Journal of Networked and Distributed Computing

Volume 1, Issue 3, July 2013, Pages 134 - 143

BSP-Based Support Vector Regression Machine Parallel Framework

Authors
Hong Zhang, Yongmei Lei
Corresponding Author
Hong Zhang
Available Online 15 January 2013.
DOI
https://doi.org/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.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Journal
International Journal of Networked and Distributed Computing
Volume-Issue
1 - 3
Pages
134 - 143
Publication Date
2013/01
ISSN (Online)
2211-7946
ISSN (Print)
2211-7938
DOI
https://doi.org/10.2991/ijndc.2013.1.3.2How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - JOUR
AU  - Hong Zhang
AU  - Yongmei Lei
PY  - 2013
DA  - 2013/01
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  - https://doi.org/10.2991/ijndc.2013.1.3.2
ID  - Zhang2013
ER  -