Abstract
Ensemble learning improves the performance of a learning machine by using a majority vote of many weak-learners. As an alternative, Miyoshi and Okada proposed ensemble-teacher learning. In this method, the student learns from many quasi-optimal teachers and performs better than the quasi-optimal teachers when a linear perceptron is used. When a non-linear perceptron is used, a Hebbian rule is effective; however, a perceptron rule is not effective in this case and the student cannot perform better than the quasi-optimal teachers. In this paper, we analyze ensemble-teacher learning and explain why a perceptron rule is not effective in ensemble-teacher learning. We propose a method to overcome this problem.
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References
Breiman, L.: Bagging predictors. Machine Learning 24, 123 (1996)
Freund, Y., Shapire, R.E.: J. Comput. Syst. Sci. 55, 119 (1997)
Murata, N., Takenouchi, T., Kanamori, T., Eguchi, S.: Information Geometry of U-Boost and Bregman Divergence. Neural Computation 16(7), 1437–1481 (2004)
Miyoshi, S., Okada, M.: Statistical mechanics of online learning for ensemble-teachers. Journal of the Physical Society of Japan 75(4), 044002 (6 pages) (2006)
Utsumi, H., Miyoshi, S., Okada, M.: Statistical Mechanics of Nonlinear On-line Learning for ensemble-teachers. J. Phys. Soc. Jpn. 76, 114001 (2007)
Okada, M., Hara, K., Miyoshi, S.: Quasi-supervised learning and ensemble learning. Meeting abstracts of the Physical Society of Japan (2007) (in Japanese)
Hara, K., Okada, M.: On-line learning through simple perceptron with a margin. Neural Networks 17, 215–223 (2004)
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Hara, K., Ono, K., Miyoshi, S. (2010). On-Line Ensemble-Teacher Learning through a Perceptron Rule with a Margin. In: Diamantaras, K., Duch, W., Iliadis, L.S. (eds) Artificial Neural Networks – ICANN 2010. ICANN 2010. Lecture Notes in Computer Science, vol 6354. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15825-4_45
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DOI: https://doi.org/10.1007/978-3-642-15825-4_45
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15824-7
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