Soft Computing

, Volume 21, Issue 9, pp 2367–2383

A method of combining forward with backward greedy algorithms for sparse approximation to KMSE

Methodologies and Application

DOI: 10.1007/s00500-015-1947-3

Cite this article as:
Zhao, YP., Liang, D. & Ji, Z. Soft Comput (2017) 21: 2367. doi:10.1007/s00500-015-1947-3
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Abstract

The prefitting and backfitting methods are commonly used to sparsify the full solution of naive kernel minimum squared error. As known to us, the forward learning methods including prefitting and backfitting only assist us in finding the suboptimal solutions. To enhance the testing real time further, in this paper by virtue of the idea of incorporating the backward learning algorithm into the forward learning algorithm, two improved schemes on the basis of prefitting and backfitting are proposed. Compared with the original versions, two improved algorithms obtain fewer significant nodes, which indicates much better testing real time. Due to the addition of the backward learning to the forward learning, the proposed algorithms need more training computational costs. Investigations on benchmark data sets and a robot arm example are reported to demonstrate the improved effectiveness.

Keywords

Kernel learning Kernel minimum squared errors Prefitting Backfitting Forward learning Backward learning 

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.College of Energy and Power EngineeringNanjing University of Aeronautics and AstronauticsNanjingChina
  2. 2.College of Physics and ElectromechanicsXiamen UniversityXiamenChina
  3. 3.School of Mechanical EngineeringNanjing University of Science and TechnologyNanjingChina

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