A resource-allocating network for function interpolation
Neural Computation
Universal approximation using radial-basis-function networks
Neural Computation
A function estimation approach to sequential learning with neural networks
Neural Computation
An efficient MDL-based construction of RBF networks
Neural Networks
Model selection in neural networks
Neural Networks
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
On Comparing Classifiers: Pitfalls toAvoid and a Recommended Approach
Data Mining and Knowledge Discovery
Fast learning in networks of locally-tuned processing units
Neural Computation
An efficient sequential learning algorithm for growing and pruning RBF (GAP-RBF) networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Scale-based clustering using the radial basis function network
IEEE Transactions on Neural Networks
Decision boundary feature extraction for neural networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Handwritten alphanumeric character recognition by the neocognitron
IEEE Transactions on Neural Networks
A Fast and Accurate Online Sequential Learning Algorithm for Feedforward Networks
IEEE Transactions on Neural Networks
Neurocontrol of nonlinear dynamical systems with Kalman filter trained recurrent networks
IEEE Transactions on Neural Networks
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Online learning neural tracker
Neurocomputing
Fast learning fully complex-valued classifiers for real-valued classification problems
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part I
Information Sciences: an International Journal
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part II
Expert Systems with Applications: An International Journal
A novel self-constructing Radial Basis Function Neural-Fuzzy System
Applied Soft Computing
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This paper presents a new sequential multi-category classifier using radial basis function (SMC-RBF) network for real-world classification problems. The classification algorithm processes the training data one by one and builds the RBF network starting with zero hidden neuron. The growth criterion uses the misclassification error, the approximation error to the true decision boundary and a distance measure between the current sample and the nearest neuron belonging to the same class. SMC-RBF uses the hinge loss function (instead of the mean square loss function) for a more accurate estimate of the posterior probability. For network parameter updates, a decoupled extended Kalman filter is used to reduce the computational overhead. Performance of the proposed algorithm is evaluated using three benchmark problems, viz., image segmentation, vehicle and glass from the UCI machine learning repository. In addition, performance comparison has also been done on two real-world problems in the areas of remote sensing and bio-informatics. The performance of the proposed SMC-RBF classifier is also compared with the other RBF sequential learning algorithms like MRAN, GAP-RBFN, OS-ELM and the well-known batch classification algorithm SVM. The results indicate that SMC-RBF produces a higher classification accuracy with a more compact network. Also, the study indicates that using a function approximation algorithm for classification problems may not work well when the classes are not well separated and the training data is not uniformly distributed among the classes.