Parameter convergence and learning curves for neural networks
Neural Computation
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Convergent Decomposition Techniques for Training RBF Neural Networks
Neural Computation
On "Natural" Learning and Pruning in Multilayered Perceptrons
Neural Computation
An iterative pruning algorithm for feedforward neural networks
IEEE Transactions on Neural Networks
A successive overrelaxation backpropagation algorithm for neural-network training
IEEE Transactions on Neural Networks
A hybrid linear/nonlinear training algorithm for feedforward neural networks
IEEE Transactions on Neural Networks
A formal selection and pruning algorithm for feedforward artificial neural network optimization
IEEE Transactions on Neural Networks
An efficient constrained training algorithm for feedforward networks
IEEE Transactions on Neural Networks
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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.