Statistical Mechanics of Linear and Nonlinear Time-Domain Ensemble Learning

DOI Web Site Web Site BIBCODE 3 Citations 26 References
  • Miyoshi Seiji
    Department of Electronic Engineering, Kobe City College of Technology
  • Okada Masato
    Division of Transdisciplinary Sciences, Graduate School of Frontier Sciences, The University of Tokyo RIKEN Brain Science Institute

Search this article

Abstract

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.

Journal

Citations (3)*help

See more

References(26)*help

See more

Related Projects

See more

Keywords

Details 詳細情報について

Report a problem

Back to top