Support Vector Machines for Pattern Classification (Advances in Pattern Recognition)
Support Vector Machines for Pattern Classification (Advances in Pattern Recognition)
Reducing examples to accelerate support vector regression
Pattern Recognition Letters
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
A study on reduced support vector machines
IEEE Transactions on Neural Networks
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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.