Support Vector Machine for Classification Based on Fuzzy Training Data

Support vector machines (SVMs) have been very successful in pattern recognition and function estimation problems, but in the support vector machines for classification, the training examples are non-fuzzy input and output is y=plusmn1;. In this paper, we introduce the support vector machine in which...

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Vydáno v:Proceedings (International Conference on Machine Learning and Cybernetics.) s. 1609 - 1614
Hlavní autoři: Ai-Bing Ji, Jia-Hong Pang, Shu-Huan Li, Jian-Ping Sun
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.08.2006
Témata:
Educational institutions
Electronic mail
fuzzy chance constraint programming
fuzzy linear separable example
fuzzy training examples
Linear programming
Machine learning
Pattern recognition
possibility measure
Quadratic programming
Sun
Support vector machine
Support vector machine classification
Support vector machines
Training data
ISBN:1424400619, 9781424400614
ISSN:2160-133X
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Shrnutí:Support vector machines (SVMs) have been very successful in pattern recognition and function estimation problems, but in the support vector machines for classification, the training examples are non-fuzzy input and output is y=plusmn1;. In this paper, we introduce the support vector machine in which the training examples are fuzzy input, and give some solving procedure of the support vector machine with fuzzy training data
ISBN:1424400619
9781424400614
ISSN:2160-133X
DOI:10.1109/ICMLC.2006.258838