Skeletonization: a technique for trimming the fat from a network via relevance assessment
Advances in neural information processing systems 1
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Advances in neural information processing systems 2
Dynamic behavior of constrained back propagation networks
Advances in neural information processing systems 2
Back propagation with expected source values
Neural Networks
Neural network fundamentals with graphs, algorithms, and applications
Neural network fundamentals with graphs, algorithms, and applications
Methods to speed up error back-propagation learning algorithm
ACM Computing Surveys (CSUR)
Optimal linear combinations of neural networks
Neural Networks
Effective backpropagation training with variable stepsize
Neural Networks
Investigation of the CasCor family of learning algorithms
Neural Networks
Algorithms and Architectures
Second-Order Methods for Neural Networks
Second-Order Methods for Neural Networks
Structure Level Adaptation for Artificial Neural Networks
Structure Level Adaptation for Artificial Neural Networks
Bayesian Regularization in Constructive Neural Networks
ICANN 96 Proceedings of the 1996 International Conference on Artificial Neural Networks
A constructive neural network algorithm for function approximation using locally fit sigmoids
A constructive neural network algorithm for function approximation using locally fit sigmoids
A review of Bayesian neural networks with an application to near infrared spectroscopy
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Objective functions for training new hidden units in constructive neural networks
IEEE Transactions on Neural Networks
Use of a quasi-Newton method in a feedforward neural network construction algorithm
IEEE Transactions on Neural Networks
A cooperative constructive method for neural networks for pattern recognition
Pattern Recognition
An Evolutionary Approach for Tuning Artificial Neural Network Parameters
HAIS '08 Proceedings of the 3rd international workshop on Hybrid Artificial Intelligence Systems
Engineering Applications of Artificial Intelligence
A node pruning algorithm based on optimal brain surgeon for feedforward neural networks
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Selection of weights for sequential feed-forward neural networks: an experimental study
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Robust training of feedforward neural networks using combined online/batch quasi-newton techniques
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II
Advances in Artificial Neural Systems
Statistical and incremental methods for neural models selection
International Journal of Artificial Intelligence and Soft Computing
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Regression problem is an important application area for neural networks (NNs). Among a large number of existing NN architectures, the feedforward NN (FNN) paradigm is one of the most widely used structures. Although one-hidden-layer feedforward neural networks (OHLFNNs) have simple structures, they possess interesting representational and learning capabilities. In this paper, we are interested particularly in incremental constructive training of OHL-FNNs. In the proposed incremental constructive training schemes for an OHL-FNN, input-side training and output-side training may be separated in order to reduce the training time. A new technique is proposed to scale the error signal during the constructive learning process to improve the input-side training efficiency and to obtain better generalization performance. Two pruning methods for removing the input-side redundant connections have also been applied. Numerical simulations demonstrate the potential and advantages of the proposed strategies when compared to other existing techniques in the literature.