Advertisement

Efficient Batch and Online Kernel Ridge Regression for Green Clouds

Conference paper
  • 1.4k Downloads
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 1)

Abstract

This study presents an energy-economic approach for incremental/decremental learning based on kernel ridge regression, a frequently used regressor on clouds. To avoid reanalyzing the entire dataset when data change, the proposed mechanism supports incremental/decremental processing for both single and multiple samples (i.e., batch processing). Experimental results showed that the performance in accuracy of the proposed method remained as well as original design. Furthermore, training time was reduced. These findings thereby demonstrate the effectiveness of the proposed method.

Keywords

Support Vector Machine Cloud Server Incremental Learning Ridge Parameter Kernel Ridge Regression 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    S.-Y. Kung, Kernel Methods and Machine Learning. Cambridge, UK: Cambridge University Press, Jun. 2014.Google Scholar
  2. 2.
    S.-Y. Kung and P.-Y. Wu, “On efficient learning and classification kernel methods,” in Proc. 2012 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2012), Kyoto, Japan, 2012, Mar. 25–30, pp. 2065–2068.Google Scholar
  3. 3.
    G. Cauwenberghs and T. Poggio, “Incremental and decremental support vector machine learning,” in Proc. 14th Annual Conf. Neural Information Processing System (NIPS 2000), Denver, Colorado, United States, 2000, Nov. 28–30, pp. 409–415.Google Scholar
  4. 4.
    C. P. Diehl and G. Cauwenberghs, “SVM incremental learning, adaptation and optimization,” in Proc. International Joint Conference on Neural Networks (IJCNN 2003), Portland, Oregon, 2003, Jul. 20–24, pp. 2685–2690.Google Scholar
  5. 5.
    J. Kivinen, A. J. Smola, and R. C. Williamson, “Online learning with kernels,” IEEE Transactions on Signal Processing, vol. 52, no. 8, pp. 2165–2176, Aug. 2004.Google Scholar
  6. 6.
    Y. Engel, S. Mannor, and R. Meir, “The kernel recursive least-squares algorithm,” IEEE Transactions on Signal Processing, vol. 52, no. 8, pp. 2275–2285, Aug. 2004.Google Scholar
  7. 7.
    P. Laskov, C. Gehl, S. Krüger, and K.-R. Müller, “Incremental support vector learning: Analysis, implementation and applications,” Journal of Machine Learning Research, vol. 7, pp. 1909–1936, 2006.Google Scholar
  8. 8.
    M. Karasuyama and I. Takeuchi, “Multiple incremental decremental learning of support vector machines,” IEEE Transactions on Neural Networks, vol. 21, no. 7, pp. 1048–1059, Jul. 2010.Google Scholar
  9. 9.
    S. V. Vaerenbergh, M. Lázaro-Gredilla, and I. Santamaría, “Kernel recursive least-squares tracker for time-varying regression,” IEEE Transactions on Neural Networks and Learning Systems, vol. 23, no. 8, pp. 1313–1326, Aug. 2012.Google Scholar
  10. 10.
    W. H. Press, S. A. Teukolsky, W. T. Vetterling, and B. P. Flannery, Numerical Recipes 3rd Edition: The Art of Scientific Computing, 3rd. ed. Cambridge, UK: Cambridge University Press, 2007.Google Scholar
  11. 11.
    K. P. Murphy, Machine Learning: A Probabilistic Perspective. Cambridge, MA, US: MIT Press, 2012.Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.School of Information TechnologyMonash UniversityMelbourneAustralia
  2. 2.Department of Media SoftwareSungkyul UniversityAnyang-siSouth Korea
  3. 3.Department of Computer Science and Computer EngineeringLa Trobe UniversityMelbourneAustralia

Personalised recommendations