Time series classification using the Volterra connectionist model and Bayes decision theory

Authors:
J. J. Rajan;P. J. W. Rayner
Affiliations:
Cambridge University Engineering Dept., Cambridge, UK;Cambridge University Engineering Dept., Cambridge, UK
Venue:
ICASSP'93 Proceedings of the 1993 IEEE international conference on Acoustics, speech, and signal processing: plenary, special, audio, underwater acoustics, VLSI, neural networks - Volume I
Year:
1993

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Abstract

This paper describes the development of a new technique for determining the weights of a Volterra Connectionist Model (VCM) applied to the classification of stationary time series. This involves assigning a classification index to each class of time series and developing expressions for the state conditional probability density functions such that the Bayes Risk can be expressed as a function of the weights. The optimal weight values then correspond to the minimum Bayes Risk.