The cascade-correlation learning architecture
Advances in neural information processing systems 2
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
A fixed-point algorithm to minimax learning with neural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A fast iterative nearest point algorithm for support vector machine classifier design
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Nonlinear kernel-based statistical pattern analysis
IEEE Transactions on Neural Networks
Bounds on the number of hidden neurons in multilayer perceptrons
IEEE Transactions on Neural Networks
A simple method to derive bounds on the size and to train multilayer neural networks
IEEE Transactions on Neural Networks
A node pruning algorithm based on a Fourier amplitude sensitivity test method
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
The geometrical learning of binary neural networks
IEEE Transactions on Neural Networks
A constructive algorithm for binary neural networks: the oil-spot algorithm
IEEE Transactions on Neural Networks
Border pairs method - constructive MLP learning classification algorithm
ICAIS'11 Proceedings of the Second international conference on Adaptive and intelligent systems
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This paper presents a new constructive algorithm to design multilayer perceptron networks used as classifiers. The resulting networks are able to classify patterns defined in a real domain. The proposed procedure allows us to automatically determine both the number of neurons and the synaptic weights of networks with a single hidden layer. The approach is based on linear programming. It avoids the typical local minima problems of error back propagation and assures convergence of the method. Furthermore, it will be shown in this paper that the presented procedure leads to single-hidden layer neural networks able to solve any problem in classifying a finite number of patterns. The performances of the proposed algorithm have been tested on some benchmark problems, and they have been compared with those of different approaches.