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Feature selection for neural networks using Parzen density estimatorA feature selection method for neural networks is proposed using the Parzen density estimator. A new feature set is selected using the decision boundary feature selection algorithm. The selected feature set is then used to train a neural network. Using a reduced feature set, an attempt is made to reduce the training time of the neural network and obtain a simpler neural network, which further reduces the classification time for test data.
Document ID
19930063779
Document Type
Conference Paper
Authors
Lee, Chulhee (Purdue Univ. West Lafayette, IN, United States)
Benediktsson, Jon A. (Univ. of Iceland Reykjavik, United States)
Landgrebe, David A. (Purdue Univ. West Lafayette, IN, United States)
Date Acquired
August 16, 2013
Publication Date
January 1, 1992
Publication Information
Publication: In: IGARSS '92; Proceedings of the 12th Annual International Geoscience and Remote Sensing Symposium, Houston, TX, May 26-29, 1992. Vol. 2 (A93-47551 20-43)
Subject Category
CYBERNETICS
Funding Number(s)
CONTRACT_GRANT: NAGW-925
Distribution Limits
Public
Copyright
Other

Related Records

IDRelationTitle19930063554Analytic PrimaryIGARSS '92; Proceedings of the 12th Annual International Geoscience and Remote Sensing Symposium, Houston, TX, May 26-29, 1992. Vols. 1 & 2