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Applied Computational Intelligence and Soft Computing
Volume 2016 (2016), Article ID 2580169, 10 pages
http://dx.doi.org/10.1155/2016/2580169
Research Article

A Novel Homogenous Hybridization Scheme for Performance Improvement of Support Vector Machines Regression in Reservoir Characterization

1Electrical Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia
2Physics Department, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia
3Physics and Electronics Department, Adekunle Ajasin University, Akungba Akoko 342111, Ondo State, Nigeria
4Computer Science Department, University of Dammam, Dammam, Saudi Arabia
5Petroleum Engineering Department, King Fahd University of Petroleum and Minerals (KFUPM), Dhahran 31261, Saudi Arabia

Received 22 November 2015; Accepted 29 March 2016

Academic Editor: Miin-Shen Yang

Copyright © 2016 Kabiru O. Akande et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

How to Cite this Article

Kabiru O. Akande, Taoreed O. Owolabi, Sunday O. Olatunji, and AbdulAzeez Abdulraheem, “A Novel Homogenous Hybridization Scheme for Performance Improvement of Support Vector Machines Regression in Reservoir Characterization,” Applied Computational Intelligence and Soft Computing, vol. 2016, Article ID 2580169, 10 pages, 2016. doi:10.1155/2016/2580169