Extreme Support Vector Regression

Chapter
Part of the Adaptation, Learning, and Optimization book series (ALO, volume 16)

Abstract

Extreme Support Vector Machine (ESVM), a variant of ELM, is a nonlinear SVM algorithm based on regularized least squares optimization. In this chapter, a regression algorithm, Extreme Support Vector Regression (ESVR), is proposed based on ESVM. Experiments show that, ESVR has a better generalization ability than the traditional ELM. Furthermore, ESVM can reach comparable accuracy as SVR and LS-SVR, but has much faster learning speed.

Keywords

Extreme learning machine Support vector regression  Extreme support vector machine Extreme support vector regression Regression 

Notes

Acknowledgments

The authors would like to thank Mr. Zhiguo Ma and Mr. Fuqiang Chen for their valuable comments. This research is partially sponsored by National Basic Research Program of China (No. 2009CB320900), and Natural Science Foundation of China (Nos. 61070116, 61070149, 61001108, 61175115, and 61272320), Beijing Natural Science Foundation (No. 4102013), President Fund of Graduate University of Chinese Academy of Sciences (No.Y35101CY00), and Importation and Development of High-Caliber Talents Project of Beijing Municipal Institutions.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Key Laboratory of Intelligent Information ProcessingInstitute of Computing Technology, Chinese Academy of SciencesBeijingChina
  2. 2.School of Computer and Control EngineeringUniversity of Chinese Academy of SciencesBeijingChina

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