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.
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References
S.-Y. Kung, Kernel Methods and Machine Learning. Cambridge, UK: Cambridge University Press, Jun. 2014.
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.
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.
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.
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.
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.
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.
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.
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.
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.
K. P. Murphy, Machine Learning: A Probabilistic Perspective. Cambridge, MA, US: MIT Press, 2012.
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Chen, BW., Rho, S., Chilamkurti, N. (2017). Efficient Batch and Online Kernel Ridge Regression for Green Clouds. In: Xhafa, F., Barolli, L., Amato, F. (eds) Advances on P2P, Parallel, Grid, Cloud and Internet Computing. 3PGCIC 2016. Lecture Notes on Data Engineering and Communications Technologies, vol 1. Springer, Cham. https://doi.org/10.1007/978-3-319-49109-7_40
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DOI: https://doi.org/10.1007/978-3-319-49109-7_40
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