Referenced Items are not available for this document.
It is common to have both observed and missing values in data. This paper concentrates on the case where a value can be somewhere between those two ends, partially observed and partially missing. To achieve that, a method of using evidence nodes in a Bayesian network is studied. Different ways of handling inaccuracies are discussed in examples and the proposed approach is justified in the experiments with real image data. Also, a justification is given for the standard preprocessing step of adding a tiny amount of noise to the data, when a continuous valued model is used for discrete-valued data.
Published in:
Neural Networks, 2004. Proceedings. 2004 IEEE International Joint Conference on
(Volume:4
)
Date of Conference: 25-29 July 2004
- Page(s):
- 2825 - 2830 vol.4
- ISSN :
- 1098-7576
- Print ISBN:
- 0-7803-8359-1
- INSPEC Accession Number:
- 8280729
- Digital Object Identifier :
- 10.1109/IJCNN.2004.1381105
- Product Type:
- Conference Publications
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