Abstract
In this paper, a new feature selection method for neural networks is proposed using the Parzen density estimator. A new feature set is selected employing the recently published decision boundary feature selection algorithm. The selected feature set is then used to train a neural network. Using a reduced feature set, we attempt to reduce the training time of the neural network and obtain a simpler neural network, further reducing the classification time for test data. Experiments show promising results.
Original language | English |
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Title of host publication | IGARSS 1992 - International Geoscience and Remote Sensing Symposium |
Subtitle of host publication | International Space Year: Space Remote Sensing |
Editors | Ruby Williamson, Tammy Stein |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 839-841 |
Number of pages | 3 |
ISBN (Electronic) | 0780301382 |
DOIs | |
Publication status | Published - 1992 |
Event | 12th Annual International Geoscience and Remote Sensing Symposium, IGARSS 1992 - Houston, United States Duration: 1992 May 26 → 1992 May 29 |
Publication series
Name | International Geoscience and Remote Sensing Symposium (IGARSS) |
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Volume | 2 |
Other
Other | 12th Annual International Geoscience and Remote Sensing Symposium, IGARSS 1992 |
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Country/Territory | United States |
City | Houston |
Period | 92/5/26 → 92/5/29 |
Bibliographical note
Funding Information:'This work was sumrted in part by NASA Grant NAGW-925.
Publisher Copyright:
© IEEE 1992.
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Earth and Planetary Sciences(all)