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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Luan Ling Lee |
| Copyright Year | 1994 |
| Description | Author affiliation: Univ. Estadual de Campinas, Sao Paulo, Brazil (Luan Ling Lee) |
| Abstract | Two new methods for feature selection and two-pattern classification by neural networks (NN) are presented. The network utilized is a single-neuron classifier. In the first approach the pocket perceptron learning algorithm is used for feature selection, and NN classifier training as well. The goals are to reduce the dimension of a feature set by selecting a subset of features of high discrimination power and to minimize the frequency of misclassification. In the second approach a modified pocket perceptron learning algorithm is used. In addition to select a subset of high discriminating features, one of the main goals of this approach is to minimize the total misclassification frequency and the false rejection error (or false acceptance error) simultaneously. The proposed methods were applied to a signature verification problem.< |
| File Size | 388811 |
| File Format | |
| ISBN | 0780317750 |
| ISSN | 15206149 |
| DOI | 10.1109/ICASSP.1994.389580 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 1994-04-19 |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Neural networks Frequency Testing Pattern classification Pattern recognition Decision theory Statistical analysis Mean square error methods Feedforward systems Neurons |
| Content Type | Text |
| Resource Type | Article |
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