Peter R. King

2009

A researcher in the Science corpus: 1 paper; trusts 1 researcher and is trusted by 0 researchers here, as inferred from the citation graph.

Whom Peter R. King trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Martin A. Riedmiller
2. M. Gevrey

Papers from Peter R. King's perspective

Ordered by how strongly the researchers Peter R. King trusts endorse each paper. Same corpus, a different order for every researcher.

1. A direct adaptive method for faster backpropagation learning: the RPROP algorithm
2. Neural Networks: A Comprehensive Foundation
3. Review and comparison of methods to study the contribution of variables in artificial neural network models
4. Adaptive pattern recognition and neural networks
5. Learning representations by back-propagating errors
6. Neural Networks and the Bias/Variance Dilemma
7. An empirical study of learning speed in back-propagation networks
8. Developing an empirical model of phytoplankton primary production: a neural network case study
9. Artificial neural network approach for modelling and prediction of algal blooms
10. Back-propagation neural networks for modeling complex systems

Their papers

• Development of artificial neural network models for predicting water saturation and fluid distribution (2009) — 89 citations