Modern Control Engineering
Application of Bayesian trained RBF networks to nonlinear time-series modeling
Signal Processing - From signal processing theory to implementation
Recognition of off-line printed Arabic text using Hidden Markov Models
Signal Processing
Classification of three-way data by the dissimilarity representation
Signal Processing
Neural networks for classification: a survey
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Handwritten digit recognition by neural networks with single-layer training
IEEE Transactions on Neural Networks
Stochastic choice of basis functions in adaptive function approximation and the functional-link net
IEEE Transactions on Neural Networks
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An optimal weight learning machine for a single-hidden layer feedforward network (SLFN) with the application to handwritten digit image recognition is developed in this paper. It is seen that both the input weights and the output weights of the SLFN are globally optimized with the batch learning type of least squares. All feature vectors of the classifier can then be placed at the prescribed positions in the feature space in the sense that the separability of all nonlinearly separable patterns can be maximized, and a high degree of recognition accuracy can be achieved with a small number of hidden nodes in the SLFN. An experiment for the recognition of the handwritten digit image from both the MNIST database and the USPS database is performed to show the excellent performance and effectiveness of the proposed methodology.