Statistical Mechanics of Linear and Nonlinear Time-Domain Ensemble Learning(General)
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概要
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Conventional ensemble learning combines students in the space domain. In this paper, however, we combine students in the time domain and call it time-domain ensemble learning. We analyze, compare, and discuss the generalization performances regarding time-domain ensemble learning of both a linear model and a nonlinear model. Analyzing in the framework of online learning using a statistical mechanical method, we show the qualitatively different behaviors between the two models. In a linear model, the dynamical behaviors of the generalization error are monotonic. We analytically show that time-domain ensemble learning is twice as effective as conventional ensemble learning. Furthermore, the generalization error of a nonlinear model features nonmonotonic dynamical behaviors when the learning rate is small. We numerically show that the generalization performance can be improved remarkably by using this phenomenon and the divergence of students in the time domain.
- 社団法人日本物理学会の論文
- 2006-12-15
著者
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Miyoshi Seiji
Department Of Electrical And Electronic Engineering Faculty Of Engineering Science Kansai University
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Okada Masato
Division Of Transdisciplinary Sciences Graduate School Of Frontier Sciences The University Of Tokyo:
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Okada Masato
Division Of Protein Metabolism Institute For Protein Research Osaka University
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