Abstract
The Support Vector Machine (SVM) has recently been introduced as a new learning technique for solving variety of real-world applications based on statistical learning theory. The classical Radial Basis Function (RBF) network has similar structure as SVM with Gaussian kernel. In this paper we have compared the generalization performance of RBF network and SVM in classification problems. We applied Lagrangian differential gradient method for training and pruning RBF network. RBF network shows better generalization performance and computationally faster than SVM with Gaussian kernel, specially for large training data sets.
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Debnath, R., Takahashi, H. (2002). Learning Capability: Classical RBF Network vs. SVM with Gaussian Kernel. In: Hendtlass, T., Ali, M. (eds) Developments in Applied Artificial Intelligence. IEA/AIE 2002. Lecture Notes in Computer Science(), vol 2358. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48035-8_29
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DOI: https://doi.org/10.1007/3-540-48035-8_29
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