Improving the classification of multiple disorders with problem decomposition
Journal of Biomedical Informatics
Dynamics of learning near singularities in layered networks
Neural Computation
Diagnosis of psychosomatic disorders using radial basis functions network
EHAC'05 Proceedings of the 4th WSEAS International Conference on Electronics, Hardware, Wireless and Optical Communications
Improving the Efficiency of Counting Defects by Learning RBF Nets with MAD Loss
ICAISC '08 Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing
MAD Loss in Pattern Recognition and RBF Learning
ICAISC '08 Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing
Technology independent circuit sizing for standard cell based design using neural networks
Digital Signal Processing
Diagnosis of Cervical Cancer Using the Median M-Type Radial Basis Function (MMRBF) Neural Network
MICAI '09 Proceedings of the 8th Mexican International Conference on Artificial Intelligence
A novel reformulated radial basis function neural network
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Pattern Recognition Letters
Radial basis function neural network based on order statistics
MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
Stacking MF networks to combine the outputs provided by RBF networks
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Median M-type radial basis function neural network
CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
Particle swarm optimization aided orthogonal forward regression for unified data modeling
IEEE Transactions on Evolutionary Computation
Pattern recognition with linearly structured labels using recursive kernel estimator
ICAISC'10 Proceedings of the 10th international conference on Artificial intelligence and soft computing: Part I
Recognition of finite structures with application to moving objects identification
ICAISC'10 Proceedings of the 10th international conference on Artificial intelligence and soft computing: Part I
Skew-Radial Basis Function Expansions for Empirical Modeling
SIAM Journal on Scientific Computing
Combination methods for ensembles of RBF networks
NN'05 Proceedings of the 6th WSEAS international conference on Neural networks
Combination methods for ensembles of RBFs
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
RBF neural network based on q-Gaussian function in function approximation
Frontiers of Computer Science in China
Gradient descent and radial basis functions
ICIC'06 Proceedings of the 2006 international conference on Intelligent Computing - Volume Part I
An experimental study on training radial basis functions by gradient descent
ANNPR'06 Proceedings of the Second international conference on Artificial Neural Networks in Pattern Recognition
Training RBFs networks: a comparison among supervised and not supervised algorithms
ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I
RBF nets in faults localization
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
Radial basis function neural networks applied to efficient QRST cancellation in atrial fibrillation
Computers in Biology and Medicine
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Presents a systematic approach for constructing reformulated radial basis function (RBF) neural networks, which was developed to facilitate their training by supervised learning algorithms based on gradient descent. This approach reduces the construction of radial basis function models to the selection of admissible generator functions. The selection of generator functions relies on the concept of the blind spot, which is introduced in the paper. The paper also introduces a new family of reformulated radial basis function neural networks, which are referred to as cosine radial basis functions. Cosine radial basis functions are constructed by linear generator functions of a special form and their use as similarity measures in radial basis function models is justified by their geometric interpretation. A set of experiments on a variety of datasets indicate that cosine radial basis functions outperform considerably conventional radial basis function neural networks with Gaussian radial basis functions. Cosine radial basis functions are also strong competitors to existing reformulated radial basis function models trained by gradient descent and feedforward neural networks with sigmoid hidden units.