Analysis of On-Line Learning when a Moving Teacher Goes around a True Teacher(General)
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概要
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In the framework of on-line learning, a learning machine might move around a teacher due to the differences in structures or output functions between the teacher and the learning machine or due to noises. The generalization performance of a new student supervised by a moving machine has been analyzed. A model composed of a fixed true teacher, a moving teacher and a student that are all linear perceptrons with noises has been treated analytically using statistical mechanics. It has been proven that the generalization errors of a student can be smaller than that of a moving teacher, even if the student only uses examples from the moving teacher.
- 社団法人日本物理学会の論文
- 2006-02-15
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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