On efficient learning and classification kernel methods

Sun-Yuan Kung, Pei Yuan Wu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Scopus citations

Abstract

Improving learning and classification efficiency has become increasingly important for machine learning. If the traditional RBF kernel is adopted, the learned kernel-based classifier usually delivers better performance by engaging a large training dataset. However, such a high performance comes at the expense of costly learning and classification complexities, which grow drastically with the training size N. To overcome this curse of dimensionality, we propose a so-called TRBF kernel(with finite intrinsic degree J) which approximates the RBF kernel. The contributions of this paper are as follows. First, the optimal classification efficiency attainable is shown to be J′ ≈ J. To improve learning efficiency, we propose a fast PDA algorithm with learning complexity linearly growing with N. We adopt pruned-PDA (PPDA) to improve the accuracy by removing harmful "anti-support" vectors from the training set. Experiments on ECG dataset showed that TRBF-PPDA delivers nearly optimal performance with very low power.

Original languageEnglish (US)
Title of host publication2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Proceedings
Pages2065-2068
Number of pages4
DOIs
StatePublished - Oct 23 2012
Event2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Kyoto, Japan
Duration: Mar 25 2012Mar 30 2012

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012
CountryJapan
CityKyoto
Period3/25/123/30/12

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

Keywords

  • PDA
  • PPDA
  • SVM
  • anti-support vectors
  • classification efficiency
  • intrinsic degree of kernels
  • learning efficiency
  • low-power on-line ECG detection

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  • Cite this

    Kung, S-Y., & Wu, P. Y. (2012). On efficient learning and classification kernel methods. In 2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Proceedings (pp. 2065-2068). [6288316] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). https://doi.org/10.1109/ICASSP.2012.6288316