Pattern Recognition Letters - Special issue on fuzzy set technology in pattern recognition
Self-organizing maps
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Three learning phases for radial-basis-function networks
Neural Networks
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Rule Extraction from Support Vector Machines
Rule Extraction from Support Vector Machines
Support vector neural training
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
Selection of prototype rules: context searching via clustering
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
Improving accuracy of LVQ algorithm by instance weighting
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part III
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Networks based on basis set function expansions, such as the Radial Basis Function (RBF), or Separable Basis Function (SBF) networks, have non-linear parameters that are not trivial to optimize. Clustering techniques are frequently used to optimize positions of localized functions. Context-dependent fuzzy clustering techniques improve convergence of parameter optimization, leading to better networks and facilitating formulation of prototype-based logical rules that provide low-complexity models of data.