On the usefulness of fuzzy SVMs and the extraction of fuzzy rules from SVMs
| Type of publication: | Inproceedings |
| Citation: | moewes_usefulness_2011 |
| Booktitle: | Proceedings of the 7th conference of the European Society for Fuzzy Logic and Technology (EUSFLAT-2011) and LFA-2011 |
| Series: | Advances in Intelligent Systems Research |
| Volume: | 17 |
| Year: | 2011 |
| Month: | July |
| Pages: | 943--948 |
| Publisher: | Atlantis Press |
| Location: | Aix-les-Bains, France |
| Organization: | European Society for Fuzzy Logic and Technology (EUSFLAT) |
| Address: | Amsterdam / Paris |
| ISSN: | 1951-6851 |
| ISBN: | 978-90-78677-00-0 |
| URL: | http://www.atlantis-press.com/... |
| DOI: | doi:10.2991/eusflat.2011.46 |
| Abstract: | In this paper we reason about the usefulness of two recent trends in fuzzy methods in machine learning. That is, we discuss both fuzzy support vector machines (FSVMs) and the extraction of fuzzy rules from SVMs. First, we show that an FSVM is identical to a special type of SVM. Second, we categorize and analyze existing approaches to obtain fuzzy rules from SVMs. Finally, we question both trends and conclude with more promising alternatives. |
| Keywords: | Binary Classification, Classification, Fuzzy Rules, fuzzy SVM, Support Vector Machine |
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| Editors | |
| Added by: | [CM] |
| Total mark: | 0 |
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