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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Chin-Teng Lin Chang-Moun Yeh Chun-Fei Hsu |
| Copyright Year | 2004 |
| Description | Author affiliation: Dept. of Electr. & Control Eng., National Chiao-Tung Univ., Hsinchu, Taiwan (Chin-Teng Lin; Chang-Moun Yeh; Chun-Fei Hsu) |
| Abstract | Fuzzy neural networks (FNNs) for pattern classification usually use the backpropagation or C-cluster type learning algorithms to learn the parameters of the fuzzy rules and membership functions from the training data. However, such kinds of learning algorithms usually cannot minimize the empirical risk (training error) and expected risk (testing error) simultaneously, and thus cannot reach a good classification performance in the testing phase. To tackle this drawback, a support-vector-based fuzzy neural network classification (SVFNNC) is proposed. The SVFNNC combines the superior classification power of support vector machine (SVM) in high reasoning of FNN in handling uncertainty information. The learning algorithm consists of two learning phases. In the phase 1, the fuzzy rules and membership functions are automatically determined by the clustering principle. In the phase 2, the parameters of FNN are calculated by the SVM with the proposed adaptive fuzzy kernel function. To investigate the effectiveness of the proposed SVFNNC, it is applied to the iris, vehicle and dna datasets. Experimental results show that the proposed SVFNNC can achieve good classification performance with drastically reduced number of fuzzy kernel functions. |
| Sponsorship | IEEE Circuits and Syst. Soc |
| File Size | 323873 |
| File Format | |
| ISBN | 078038251X |
| DOI | 10.1109/ISCAS.2004.1329910 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2004-05-23 |
| Publisher Place | Canada |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Fuzzy neural networks Support vector machines Support vector machine classification Backpropagation algorithms Testing Kernel Pattern classification Training data Uncertainty Clustering algorithms |
| Content Type | Text |
| Resource Type | Article |
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