Advances in neural information processing systems 2
Dynamic behavior of constrained back propagation networks
Advances in neural information processing systems 2
Generalization by weight-elimination with application to forecasting
NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
Improving Generalization with Active Learning
Machine Learning - Special issue on structured connectionist systems
Determining the Significance of Input Parameters using Sensitivity Analysis
IWANN '96 Proceedings of the International Workshop on Artificial Neural Networks: From Natural to Artificial Neural Computation
IWANN '93 Proceedings of the International Workshop on Artificial Neural Networks: New Trends in Neural Computation
Sensitivity Analysis for Selective Learning by Feedforward Neural Networks
Fundamenta Informaticae
Sensitivity Analysis for Selective Learning by Feedforward Neural Networks
Fundamenta Informaticae
Sensitivity Analysis for Selective Learning by Feedforward Neural Networks
Fundamenta Informaticae
Sensitivity Analysis for Selective Learning by Feedforward Neural Networks
Fundamenta Informaticae
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A novel approach is presented to visualize and analyze decision boundariesfor feedforward neural networks. First order sensitivity analysis of theneural network output function with respect to input perturbations is usedto visualize the position of decision boundaries over input space. Similarly,sensitivity analysis of each hidden unit activation function reveals whichboundary is implemented by which hidden unit. The paper shows how thesesensitivity analysis models can be used to better understand the data beingmodelled, and to visually identify irrelevant input and hidden units.