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A study on reduced support vector machines.

Kuan-Ming Lin1, Chih-Jen Lin

  • 1Dept. of Comput. Sci. and Inf. Eng., Nat. Taiwan Univ., Taipei, Taiwan.

IEEE Transactions on Neural Networks
|February 5, 2008
PubMed
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Reduced Support Vector Machines (RSVM) offer a faster alternative for large datasets but show slightly lower accuracy than standard Support Vector Machines (SVM). RSVM is most beneficial for very large problems or those with numerous support vectors.

Area of Science:

  • Machine Learning
  • Data Mining
  • Computational Science

Background:

  • Standard Support Vector Machines (SVM) face challenges with large datasets, especially with nonlinear kernels.
  • Reduced Support Vector Machines (RSVM) were proposed to address SVM's scalability by preselecting support vectors and solving smaller optimization problems.

Purpose of the Study:

  • To investigate the practical utility and performance of RSVM compared to standard SVM.
  • To analyze the generalization ability and training time efficiency of RSVM.
  • To compare different RSVM implementations and SVM variants.

Main Methods:

  • The study demonstrates that RSVM formulations are equivalent to linear SVM.
  • Four distinct RSVM implementations were analyzed.

Related Experiment Videos

  • Empirical experiments were conducted to compare RSVM and standard SVM performance.

    • Main Results:

    • RSVM generally exhibits slightly lower test accuracy than standard SVM.
    • For datasets up to tens of thousands of data points, existing SVM implementations are competitive in training time if the support vector percentage is low.
    • RSVM's advantages are most pronounced for very large datasets or those with a high proportion of support vectors.

    Conclusions:

    • RSVM is primarily advantageous for extremely large-scale machine learning problems or scenarios with a significant number of support vectors.
    • The study provides empirical comparisons of linear SVM implementations and SVM with different cost functions.