Matrix computations (3rd ed.)
The art of computer programming, volume 3: (2nd ed.) sorting and searching
The art of computer programming, volume 3: (2nd ed.) sorting and searching
Second-Order Methods for Neural Networks
Second-Order Methods for Neural Networks
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Proceedings of Fuzzy Logik, Theorie und Praxis, 4. Dortmunder Fuzzy-Tage
A Theory of Networks for Approximation and Learning
A Theory of Networks for Approximation and Learning
Fast learning in networks of locally-tuned processing units
Neural Computation
IEEE Transactions on Neural Networks
RBF neural network center selection based on Fisher ratio class separability measure
IEEE Transactions on Neural Networks
Technical data mining with evolutionary radial basis function classifiers
Applied Soft Computing
Information Sciences: an International Journal
Techniques for knowledge acquisition in dynamically changing environments
ACM Transactions on Autonomous and Adaptive Systems (TAAS) - Special section on formal methods in pervasive computing, pervasive adaptation, and self-adaptive systems: Models and algorithms
Learning from others: Exchange of classification rules in intelligent distributed systems
Artificial Intelligence
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Radial basis function (RBF) networks are used in many applications, e.g. for pattern classification or nonlinear regression. Typically, either stochastic, iterative training algorithms (e.g. gradient-based or second-order techniques) or clustering methods in combination with a linear optimisation technique (e.g. c-means and singular value decomposition for a linear least-squares problem) are applied to find the parameters (centres, radii and weights) of an RBF network. This article points out the advantages of a combination of the two approaches and describes a modification of the standard c-means algorithm that leads to a linear least-squares problem for which solvability can be guaranteed. The first idea may lead to significant improvements concerning the training time as well as the approximation and generalisation properties of the networks. In the particular application problem investigated here (intrusion detection in computer networks), the overall training time could be reduced by about 29% and the error rate could be reduced by about 74%. The second idea rises the reliability of the training procedure at no additional costs (regarding both, run time and quality of results).