HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Full Text Full Text (PDF) Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by de Freitas, J. F. G. Articles by Doucet, A. Search for Related Content PubMed PubMed Citation Articles by de Freitas, J. F. G. Articles by Doucet, A.

Sequential Monte Carlo Methods to Train Neural Network Models

Cambridge University Engineering Department, Cambridge CB2 1PZ, England, U.K.

M. Niranjan

Cambridge University Engineering Department, Cambridge CB2 1PZ, England, U.K.

A. H. Gee

Cambridge University Engineering Department, Cambridge CB2 1PZ, England, U.K.

A. Doucet

Cambridge University Engineering Department, Cambridge CB2 1PZ, England, U.K.

We discuss a novel strategy for training neural networks using sequential Monte Carlo algorithms and propose a new hybrid gradient descent/sampling importance resampling algorithm (HySIR). In terms of computational time and accuracy, the hybrid SIR is a clear improvement over conventional sequential Monte Carlo techniques. The new algorithm may be viewed as a global optimization strategy that allows us to learn the probability distributions of the network weights and outputs in a sequential framework. It is well suited to applications involving on-line, nonlinear, and nongaussian signal processing. We show how the new algorithm outperforms extended Kalman filter training on several problems. In particular, we address the problem of pricing option contracts, traded in financial markets. In this context, we are able to estimate the one-step-ahead probability density functions of the options prices.

This article has been cited by other articles:

				H. CAO, N. OHNISHI, Y. TAKEUCHI, T. MATSUMOTO, and H. KUDO Gauss-Newton Particle Filter IEICE Trans A: Fundamentals, June 1, 2007; E90-A(6): 1235 - 1239. [Abstract] [PDF]

				C. Andrieu, N. de Freitas, and A. Doucet Robust Full Bayesian Learning for Radial Basis Networks Neural Comput., October 1, 2001; 13(10): 2359 - 2407. [Abstract] [Full Text] [PDF]