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Information Technology Journal
  Year: 2008 | Volume: 7 | Issue: 2 | Page No.: 370-373
DOI: 10.3923/itj.2008.370.373
An Improved Algorithm on Least Squares Support Vector Machines
Wang Liejun, Lai Huicheng and Zhang Taiyi

Abstract:
The Least Squares Support Vector Machines (LS-SVM) is an improvement on the Support Vector Machines (SVM). Combined the LS-SVM with the Multi-Resolution Analysis (MRA), an improved algorithm-the Multi-resolution Least Squares Support Vector Machines (MLS-SVM) algorithm is proposed in this study. With better approximation ability, the proposed algorithm has the same theoretical framework as the MRA. At a fixed scale the MLS-SVM is a classical LS-SVM. However, the MLS-SVM can gradually approximate the target function at different scales. In experiment, the MLS-SVM is used as nonlinear system`s identification, with better identification accuracy achieved.
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How to cite this article:

Wang Liejun, Lai Huicheng and Zhang Taiyi, 2008. An Improved Algorithm on Least Squares Support Vector Machines. Information Technology Journal, 7: 370-373.

DOI: 10.3923/itj.2008.370.373

URL: https://scialert.net/abstract/?doi=itj.2008.370.373

 
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