Volume 1, Issue 3, August 2013, Pages 134 - 143
BSP-Based Support Vector Regression Machine Parallel Framework
Hong Zhang, Yongmei Lei
Received 16 April 2013, Accepted 15 June 2013, Available Online 1 August 2013.
- https://doi.org/10.2991/ijndc.2013.1.3.2How to use a DOI?
- parallel computing; bulk synchronous parallel; support vector regression machine (SVR); regression prediction.
- 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.
Cite this article
TY - JOUR AU - Hong Zhang AU - Yongmei Lei PY - 2013 DA - 2013/08 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 -