Analysis of on-line learning when a moving teacher goes around a true teacher

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.

arXiv comment13 pages, 8 figures

References in corpus (5)

• On-Line AdaTron Learning of Unlearnable Rules
• Analysis of ensemble learning using simple perceptrons based on online learning theory
• Online Learning with Ensembles
• Ensemble learning of linear perceptron; Online learning theory
• A Simple Perceptron that Learns Non-Monotonic Rules

Cited by in corpus (6)

• Statistical Mechanics of Online Learning for Ensemble Teachers
• Statistical Mechanics of On-line Learning when a Moving Teacher Goes around an Unlearnable True Teacher
• Statistical Mechanics of Nonlinear On-line Learning for Ensemble Teachers
• On-line Learning of an Unlearnable True Teacher through Mobile Ensemble Teachers
• Statistical Mechanics of Time Domain Ensemble Learning
• Statistical Mechanics of Linear and Nonlinear Time-Domain Ensemble Learning