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.
|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.|
|Number of pages||3|
|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
|Name||International Geoscience and Remote Sensing Symposium (IGARSS)|
|Other||12th Annual International Geoscience and Remote Sensing Symposium, IGARSS 1992|
|Period||92/5/26 → 92/5/29|
Bibliographical noteFunding Information:
'This work was sumrted in part by NASA Grant NAGW-925.
© IEEE 1992.
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Earth and Planetary Sciences(all)