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A fast iterative nearest point algorithm for support vector machine classifier design.

S S Keerthi1, S K Shevade, C Bhattacharyya

  • 1Department of Mechanical and Production Engineering, National University of Singapore, Singapore. mpessk@guppy.mpe.nus.edu.sg

IEEE Transactions on Neural Networks
|February 6, 2008
PubMed
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This study introduces a novel, fast iterative algorithm for Support Vector Machine (SVM) classification. The new method efficiently solves nearest point problems between convex polytopes, offering competitive performance against existing SVM techniques.

Area of Science:

  • Machine Learning
  • Computational Geometry
  • Optimization

Background:

  • Support Vector Machines (SVMs) are powerful classification algorithms.
  • Existing SVM algorithms can be computationally intensive.
  • Handling classification violations is a key challenge in SVM design.

Purpose of the Study:

  • To develop a new, fast iterative algorithm for Support Vector Machine (SVM) classifier design.
  • To address SVM problems with and without classification violations.
  • To compare the new algorithm's performance against established SVM methods.

Main Methods:

  • The core problem is reformulated as finding the nearest point between two convex polytopes.
  • Classical nearest point algorithms (Gilbert, Mitchell et al.) are studied and combined.

Related Experiment Videos

  • Quadratic penalization is used to handle classification violations, adapting techniques from Cortes, Vapnik, and Friess.

    • Main Results:

    • A novel, fast iterative algorithm for SVM classification is derived.
    • The algorithm effectively handles problems with and without classification violations.
    • Computational evaluations demonstrate the algorithm's competitiveness against Platt's sequential minimal optimization.

    Conclusions:

    • The proposed fast iterative algorithm offers an efficient approach to SVM classifier design.
    • This method provides a competitive alternative to existing powerful SVM techniques.
    • The algorithm's ability to handle classification violations enhances its practical applicability.