Referenced Items are not available for this document.
Classification accuracy improvement of neural network classifiers using unlabeled testing data is presented. In order to increase the classification accuracy without increasing the number of training data, the network makes use of testing data along with training data for learning. It is shown that including the unlabled samples from underrepresented classes in the training set improves the classification accuracy of some of the classes during supervised-unsupervised learning
Published in:
Geoscience and Remote Sensing, IEEE Transactions on
(Volume:36
,
Issue:
3
)
Date of Publication: May 1998
- Page(s):
- 1020 - 1025
- ISSN :
- 0196-2892
- INSPEC Accession Number:
- 5929325
- Digital Object Identifier :
- 10.1109/36.673695
- Product Type:
- Journals & Magazines
- Date of Current Version :
- 06 August 2002
- Issue Date :
- May 1998
- Sponsored by :
- IEEE Geoscience and Remote Sensing Society
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