Name
Abstract | ||
|---|---|---|
We have developed a linguistic perceptron (LP) to deal with the problem in pattern recognition where inputs are uncertain. This algorithm is based on the extension principle and the decomposition theorem. Several synthetic data sets are used to illustrate the behavior of this linguistic perceptron in linearly separable, nonlinearly separable and nonseparable situations. We also compare the results from the linguistic perceptron with that from the regular perceptron. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1109/FUZZY.2006.1681868 | Vancouver, BC |
Keywords | Field | DocType |
boundary-value problems,fuzzy set theory,pattern recognition,perceptrons,decomposition theorem,extension principle,linguistic perceptron,nonlinear decision boundary problem,pattern recognition,synthetic data set | Linear separability,Boundary value problem,Nonlinear system,Computer science,Separable space,Fuzzy set,Multilayer perceptron,Artificial intelligence,Perceptron,Linguistics,Decision boundary,Machine learning | Conference |
ISSN | ISBN | Citations |
1098-7584 | 0-7803-9488-7 | 1 |
PageRank | References | Authors |
0.37 | 10 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| S. Auephanwiriyakul | 1 | 246 | 39.45 |
| Sompong Dhompongsa | 2 | 1 | 0.37 |