Abstract
SVM1 and FNN2 are popular techniques for pattern classification. SVM has excellent generalization performance, but this performance is dependent on appropriate determining its kernel function. FNN is equipped with human-like reasoning, but the learning algorithms used in most FNN classifiers only focus on minimizing empirical risk. In this paper, a new classifier called ASVMFC has offered uses capabilities of SVM and FNN together and does not have the mentioned disadvantages. In fact, ASVMFC is a fuzzy neural network that its parameters is adjusted using a SVM with an adaptive kernel function. ASVMFC uses a new clustering algorithm to make up its fuzzy rules. Moreover, an efficient sampling method has been introduced in this paper that drastically reduces the number of training samples with very slight impact on the performance of ASVMFC. The experimental results illustrate ASVMFC can achieve very good classification accuracy with generating only a few fuzzy rules.
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Ganji, H., Khadivi, S. (2011). ASVMFC: Adaptive Support Vector Machine Based Fuzzy Classifier. In: Salem, M.V.M., Shaalan, K., Oroumchian, F., Shakery, A., Khelalfa, H. (eds) Information Retrieval Technology. AIRS 2011. Lecture Notes in Computer Science, vol 7097. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25631-8_31
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DOI: https://doi.org/10.1007/978-3-642-25631-8_31
Publisher Name: Springer, Berlin, Heidelberg
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