Support Vector Machine Based on Type-2 Fuzzy Training Samples
In order to deal with the classification problems of type-2 fuzzy training samples on generalized credibility space. Firstly the type-2 fuzzy training samples are reduced to ordinary fuzzy samples by the mean reduction method. Secondly the definition of strong fuzzy linear separable data for type-2...
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| Vydáno v: | Industrial Engineering & Management Systems Ročník 11; číslo 1; s. 26 - 29 |
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| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
대한산업공학회
01.03.2012
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| Témata: | |
| ISSN: | 1598-7248, 2234-6473 |
| On-line přístup: | Získat plný text |
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| Shrnutí: | In order to deal with the classification problems of type-2 fuzzy training samples on generalized credibility space.
Firstly the type-2 fuzzy training samples are reduced to ordinary fuzzy samples by the mean reduction method. Secondly the definition of strong fuzzy linear separable data for type-2 fuzzy samples on generalized credibility space is introduced. Further, by utilizing fuzzy chance-constrained programming and classic support vector machine, a support vector machine based on type-2 fuzzy training samples and established on generalized credibility space is given. An example shows the efficiency of the support vector machine. KCI Citation Count: 0 |
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| Bibliografie: | G704-002162.2012.11.1.015 |
| ISSN: | 1598-7248 2234-6473 |
| DOI: | 10.7232/iems.2012.11.1.026 |