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Training nu-support vector classifiers: theory and algorithms.

C C Chang1, C J Lin

  • 1Department of Computer Science and Information Engineering, National Taiwan University, Taipei 106, Taiwan.

Neural Computation
|August 23, 2001
PubMed
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The nu-support vector machine (nu-SVM) offers control over support vectors. This study introduces an effective decomposition method for large-scale nu-SVM, making it competitive with C-SVM methods.

Area of Science:

  • Machine Learning
  • Computational Statistics
  • Pattern Recognition

Background:

  • The nu-support vector machine (nu-SVM) is a classification algorithm.
  • A key feature of nu-SVM is the parameter nu, which controls the number of support vectors.
  • nu-SVM and C-support vector machine (C-SVM) are related but distinct problems.

Purpose of the Study:

  • To investigate the relationship between nu-SVM and C-SVM.
  • To address the lack of effective methods for solving large-scale nu-SVM.
  • To propose a novel decomposition method for nu-SVM.

Main Methods:

  • Detailed theoretical analysis comparing nu-SVM and C-SVM.
  • Development of a decomposition method tailored for nu-SVM.
  • Numerical experiments to evaluate the proposed method's performance.

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Main Results:

  • nu-SVM and C-SVM generally represent different optimization problems but share the same optimal solution set.
  • The proposed decomposition method for nu-SVM demonstrates competitive performance against existing C-SVM methods.
  • Numerical experiments validate the effectiveness of the new approach for large-scale nu-SVM.

Conclusions:

  • The proposed decomposition method significantly enhances the solvability of large-scale nu-SVM problems.
  • The findings suggest that nu-SVM can be as computationally efficient as C-SVM for large datasets.
  • This work bridges a gap in efficient algorithms for nu-SVM, promoting its wider application.