Generalizing smoothness constraints from discrete samples
Neural Computation
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Creating artificial neural networks that generalize
Neural Networks
Advances in neural information processing systems 2
Simplifying neural networks by soft weight-sharing
Neural Computation
Structural learning with forgetting
Neural Networks
Structure and properties of generalized adaptive neural filters for signal enhancement
IEEE Transactions on Neural Networks
An iterative pruning algorithm for feedforward neural networks
IEEE Transactions on Neural Networks
Neural Edge Detector - A Good Mimic of Conventional One Yet Robuster against Noise
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Bio-inspired Applications of Connectionism-Part II
Forward and Backward Selection in Regression Hybrid Network
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
Neural Edge Enhancer for Supervised Edge Enhancement from Noisy Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
A novel pruning algorithm for self-organizing neural network
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
3D virtual colonoscopy for polyps detection by supervised artificial neural networks
ICIC'11 Proceedings of the 7th international conference on Intelligent Computing: bio-inspired computing and applications
Pixel-based machine learning in medical imaging
Journal of Biomedical Imaging - Special issue on Machine Learning in Medical Imaging
Generalization capability of artificial neural network incorporated with pruning method
ADCONS'11 Proceedings of the 2011 international conference on Advanced Computing, Networking and Security
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This paper describes an approach to synthesizing desired filters using a multilayer neural network (NN). In order to acquire the right function of the object filter, a simple method for reducing the structures of both the input and the hidden layers of the NN is proposed. In the proposed method, the units are removed from the NN on the basis of the influence of removing each unit on the error, and the NN is retrained to recover the damage of the removal. Each process is performed alternately, and then the structure is reduced. Experiments to synthesize a known filter were performed. By the analysis of the NN obtained by the proposed method, it has been shown that it acquires the right function of the object filter. By the experiment to synthesize the filter for solving real signal processing tasks, it has been shown that the NN obtained by the proposed method is superior to that obtained by the conventional method in terms of the filter performance and the computational cost.