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Reducing SVM classification time using multiple mirror classifiers.

Jiun-Hung Chen1, Chu-Song Chen

  • 1Department of Computer Science and Engineering, University of Washington, Seattle, WA 98195, USA. jhchen@cs.washington.edu

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|September 21, 2004
PubMed
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This study introduces a novel method using multiple simple classifiers to accelerate support vector machine (SVM) decisions. The approach maintains high classification accuracy while significantly reducing processing time for SVM models.

Area of Science:

  • Computer Science
  • Machine Learning
  • Artificial Intelligence

Background:

  • Support Vector Machines (SVMs) are powerful classification algorithms but can be computationally intensive.
  • Reducing the classification time of SVMs is crucial for real-time applications and large datasets.

Purpose of the Study:

  • To develop an efficient method for accelerating SVM classification.
  • To maintain high classification accuracy while decreasing computational time.

Main Methods:

  • Utilizing mirror point pairs to construct multiple simple classifiers.
  • Implementing a coarse-to-fine selection strategy for member classifiers, involving clustering and a greedy approach.
  • Refining selected classifiers through a weighted combination with a perceptron.

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Main Results:

  • The proposed approach successfully reduces SVM classification time.
  • Classification accuracy is maintained at a level comparable to a single SVM.

Conclusions:

  • The multiple classifier system effectively approximates SVM decision rules.
  • This method offers a viable solution for speeding up SVM computations without sacrificing performance.