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Related Experiment Videos

Preliminary study on wilcoxon learning machines.

J G Hsieh1, Y L Lin, J H Jeng

  • 1Department of Electrical Engineering, National Sun Yat-Sen University, Taiwan. jghsieh@mail.ee.nsysu.edu.tw

IEEE Transactions on Neural Networks
|February 14, 2008
PubMed
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The Wilcoxon approach, known for statistical robustness, is introduced to machine learning. New Wilcoxon learning machines demonstrate strong resistance to outliers in nonlinear problems.

Area of Science:

  • Machine Learning
  • Statistical Modeling

Background:

  • Rank-based Wilcoxon regression is robust to outliers.
  • This robustness motivates extending the Wilcoxon approach to machine learning.

Purpose of the Study:

  • Introduce the Wilcoxon approach to machine learning.
  • Investigate novel Wilcoxon-based learning machines for nonlinear problems.

Main Methods:

  • Developed four new learning machines: Wilcoxon neural network (WNN), Wilcoxon generalized radial basis function network (WGRBFN), Wilcoxon fuzzy neural network (WFNN), and kernel-based Wilcoxon regressor (KWR).
  • Derived simple gradient descent-based weight updating rules.
  • Conducted numerical simulations to compare outlier robustness.

Main Results:

Related Experiment Videos

  • Wilcoxon learning machines exhibit good robustness against outliers.
  • Performance was compared against various existing learning machines.

Conclusions:

  • The Wilcoxon approach offers a promising methodology for machine learning.
  • Proposed Wilcoxon learning machines are effective for nonlinear problems with outliers.