Classification of incomplete feature vectors by radial basis function networks

Dybowski, Richard (1998) ‘Classification of incomplete feature vectors by radial basis function networks’, Pattern Recognition Letters, 19(14), pp. 1257-1264.

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Dybowski R.(1998) Pattern Recognition Letters 19 (14) 1257-1264.pdf - Accepted Version
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Official URL: http://dx.doi.org/10.1016/S0167-8655(98)00096-8

Abstract

The paper describes the use of radial basis function neural networks with Gaussian basis functions to classify incomplete feature vectors. The method uses the fact that any marginal distribution of a Gaussian distribution can be determined from the mean vector and covariance matrix of the joint distribution.

Item Type: Article
Additional Information: Citation: Dybowski R. (1998) "Classification of incomplete feature vectors by radial basis function networks". Pattern Recognition Letters, 19 (14) 1257-1264.
Divisions: Schools > Architecture Computing and Engineering, School of
Depositing User: Mr Stephen Grace
Date Deposited: 02 Nov 2009 14:39
Last Modified: 27 Sep 2012 11:58
URI: http://hdl.handle.net/10552/369

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