The theory of on-line learning: a statistical physics
approach

David Saad

The theory of on-line learning: a statistical physics approach

Aston University

Research output: Chapter in Book/Published conference output › Chapter

Abstract

In this paper we review recent theoretical approaches for analysing the dynamics of on-line learning in multilayer neural networks using methods adopted from statistical physics. The analysis is based on monitoring a set of macroscopic variables from which the generalisation error can be calculated. A closed set of dynamical equations for the macroscopic variables is derived analytically and solved numerically. The theoretical framework is then employed for defining optimal learning parameters and for analysing the incorporation of second order information into the learning process using natural gradient descent and matrix-momentum based methods. We will also briefly explain an extension of the original framework for analysing the case where training examples are sampled with repetition.

Original language	English
Title of host publication	Exploratory Data Analysis in Empirical Research: Proceedings of the 25th Annual Conference of the Gesellschaft für Klassifikation e.V., University of Munich, March 14-16, 2001
Editors	Manfred Schwaiger, Otto Opit
Publisher	Springer
Pages	300-309
Number of pages	10
ISBN (Print)	9783540441830
Publication status	Published - 2003
Event	Studies in Classification, Data Analysis and Knowledge Organization - Duration: 1 Jan 2003 → 1 Jan 2003

Publication series

Name	Studies in Classification, Data Analysis, and Knowledge Organization
Publisher	Springer-Verlag

Other

Other	Studies in Classification, Data Analysis and Knowledge Organization
Period	1/01/03 → 1/01/03

Bibliographical note

The original publication is available at www.springerlink.com

Keywords

on-line learning
neural networks
statistical physics
natural gradient descent
matrix-momentum
repetition

Access to Document

NCRG_2001_010.pdf
The original publication is available at www.springerlink.com
Accepted author manuscript, 192 KB

Cite this

Saad, D. (2003). The theory of on-line learning: a statistical physics approach. In M. Schwaiger, & O. Opit (Eds.), Exploratory Data Analysis in Empirical Research: Proceedings of the 25th Annual Conference of the Gesellschaft für Klassifikation e.V., University of Munich, March 14-16, 2001 (pp. 300-309). (Studies in Classification, Data Analysis, and Knowledge Organization). Springer.

Saad, David. / The theory of on-line learning: a statistical physics approach. Exploratory Data Analysis in Empirical Research: Proceedings of the 25th Annual Conference of the Gesellschaft für Klassifikation e.V., University of Munich, March 14-16, 2001. editor / Manfred Schwaiger ; Otto Opit. Springer, 2003. pp. 300-309 (Studies in Classification, Data Analysis, and Knowledge Organization).

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keywords = "on-line learning, neural networks, statistical physics, natural gradient descent, matrix-momentum, repetition",

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Saad, D 2003, The theory of on-line learning: a statistical physics approach. in M Schwaiger & O Opit (eds), Exploratory Data Analysis in Empirical Research: Proceedings of the 25th Annual Conference of the Gesellschaft für Klassifikation e.V., University of Munich, March 14-16, 2001. Studies in Classification, Data Analysis, and Knowledge Organization, Springer, pp. 300-309, Studies in Classification, Data Analysis and Knowledge Organization, 1/01/03.

The theory of on-line learning: a statistical physics approach. / Saad, David.
Exploratory Data Analysis in Empirical Research: Proceedings of the 25th Annual Conference of the Gesellschaft für Klassifikation e.V., University of Munich, March 14-16, 2001. ed. / Manfred Schwaiger; Otto Opit. Springer, 2003. p. 300-309 (Studies in Classification, Data Analysis, and Knowledge Organization).

Research output: Chapter in Book/Published conference output › Chapter

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Saad D. The theory of on-line learning: a statistical physics approach. In Schwaiger M, Opit O, editors, Exploratory Data Analysis in Empirical Research: Proceedings of the 25th Annual Conference of the Gesellschaft für Klassifikation e.V., University of Munich, March 14-16, 2001. Springer. 2003. p. 300-309. (Studies in Classification, Data Analysis, and Knowledge Organization).