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Improving support vector machine classifiers by modifying kernel functions.

S Amari1, S Wu

  • 1RIKEN Brain Science Institute, The Institute for Physical and Chemical Research, Hirosawa 2-1, Wako-shi, Saitama, Japan

Neural Networks : the Official Journal of the International Neural Network Society
|March 29, 2003
PubMed
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We introduce a novel method to enhance support vector machine (SVM) classifier performance by modifying kernel functions. This approach improves class separability using Riemannian geometry, leading to better generalization on diverse datasets.

Area of Science:

  • Machine Learning
  • Computational Geometry
  • Kernel Methods

Background:

  • Support Vector Machines (SVMs) are powerful classifiers.
  • Kernel functions define the feature space in SVMs.
  • Improving SVM generalization is crucial for real-world applications.

Purpose of the Study:

  • To propose a novel method for enhancing SVM classifier performance.
  • To leverage Riemannian geometry for kernel function modification.
  • To increase class separability by adjusting spatial resolution.

Main Methods:

  • Modifying kernel functions based on induced Riemannian geometry.
  • Utilizing conformal mapping to enlarge spatial resolution near the decision boundary.
  • Applying the method to Gaussian Radial Basis Function (RBF) kernels.

Related Experiment Videos

Main Results:

  • Demonstrated significant improvement in SVM classifier performance.
  • Reduced generalization errors on both artificial and real-world datasets.
  • Validated the effectiveness of the proposed kernel modification technique.

Conclusions:

  • The proposed kernel modification method effectively enhances SVM performance.
  • Riemannian geometry provides a theoretical basis for improving kernel-based classification.
  • The technique shows promise for broader applications in machine learning.