## Abstract

Fuzziness must be considered in systems where human estimation is influential. A model of such a vague phenomenon might be represented as a fuzzy system equation which can be described by the fuzzy functions defined by Zadeh's extension principle. In this paper, we incorporate the concept of fuzzy set theory into the support vector machine (SVM) regression. The parameters to be identified in SVM regression, such as the components within the weight vector and the bias term, are fuzzy numbers, and the desired outputs in training samples are also fuzzy numbers. This integration preserves the benefits of SVM regression model and fuzzy regression model, where the SVM learning theory characterizes properties of learning machines which enable them to generalize well the unseen data and the fuzzy set theory might be very useful for finding a fuzzy structure in an evaluation system.

Original language | English |
---|---|

Pages | 738-742 |

Number of pages | 5 |

Publication status | Published - 2003 Jul 11 |

Event | The IEEE International conference on Fuzzy Systems - St. Louis, MO, United States Duration: 2003 May 25 → 2003 May 28 |

### Other

Other | The IEEE International conference on Fuzzy Systems |
---|---|

Country/Territory | United States |

City | St. Louis, MO |

Period | 03-05-25 → 03-05-28 |

## All Science Journal Classification (ASJC) codes

- Software
- Theoretical Computer Science
- Artificial Intelligence
- Applied Mathematics