Analysis of on-line learning when a moving teacher goes around a true teacher
Physics and Society
2009-11-11 v1 Disordered Systems and Neural Networks
Abstract
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 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.
Cite
@article{arxiv.physics/0509050,
title = {Analysis of on-line learning when a moving teacher goes around a true teacher},
author = {Seiji Miyoshi and Masato Okada},
journal= {arXiv preprint arXiv:physics/0509050},
year = {2009}
}
Comments
13 pages, 8 figures