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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Keller, James M. Hunt, Douglas J. |
| Copyright Year | 1979 |
| Abstract | The perceptron algorithm, one of the class of gradient descent techniques, has been widely used in pattern recognition to determine linear decision boundaries. While this algorithm is guaranteed to converge to a separating hyperplane if the data are linearly separable, it exhibits erratic behavior if the data are not linearly separable. Fuzzy set theory is introduced into the perceptron algorithm to produce a ``fuzzy algorithm'' which ameliorates the convergence problem in the nonseparable case. It is shown that the fuzzy perceptron, like its crisp counterpart, converges in the separable case. A method of generating membership functions is developed, and experimental results comparing the crisp to the fuzzy perceptron are presented. |
| Sponsorship | IEEE Computer Society |
| Starting Page | 693 |
| Ending Page | 699 |
| Page Count | 7 |
| File Size | 1778440 |
| File Format | |
| ISSN | 01628828 |
| Volume Number | PAMI-7 |
| Issue Number | 6 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 1985-11-01 |
| Publisher Place | U.S.A. |
| Access Restriction | One Nation One Subscription (ONOS) |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Fuzzy sets Vectors Iterative algorithms Pattern recognition Machine learning algorithms Fuzzy set theory Convergence Machine learning Pattern analysis separating hyperplane fuzzy 2-means gradient descent induced fuzzy membership iterative training perceptron algorithm |
| Content Type | Text |
| Resource Type | Article |
| Subject | Applied Mathematics Artificial Intelligence Computational Theory and Mathematics Computer Vision and Pattern Recognition Software |
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