Improving Generalization with Active Learning
Machine Learning - Special issue on structured connectionist systems
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Uniform object generation for optimizing one-class classifiers
The Journal of Machine Learning Research
Sensitivity Analysis for Selective Learning by Feedforward Neural Networks
Fundamenta Informaticae
A new methodology of extraction, optimization and application of crisp and fuzzy logical rules
IEEE Transactions on Neural Networks
Variable step search algorithm for feedforward networks
Neurocomputing
A Comparison of Methods for Learning of Highly Non-separable Problems
ICAISC '08 Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing
Building Localized Basis Function Networks Using Context Dependent Clustering
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
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SVM learning strategy based on progressive reduction of the number of training vectors is used for MLP training. Threshold for acceptance of useful vectors for training is dynamically adjusted during learning, leading to a small number of support vectors near decision borders and higher accuracy of the final solutions. Two problems for which neural networks have previously failed to provide good results are presented to illustrate the usefulness of this approach.