Hong-gui Han

Artificial Intelligence · 2013

A researcher in the Science corpus: 1 paper; trusts 14 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 9 citations into it
• Computer Vision and Pattern Recognition — 6 citations into it

Whom Hong-gui Han trusts overall

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

1. G. Huang
2. Andrea Saltelli
3. Babak Hassibi
4. Lei Chen
5. Guorui Feng
6. M. Islam
7. Yoan Miché
8. David G. Stork
9. Mikko Lehtokangas
10. Paramasivan Saratchandran

Papers from Hong-gui Han's perspective

Ordered by how strongly the researchers Hong-gui Han trusts endorse each paper. Same corpus, a different order for every researcher.

1. Universal Approximation using Incremental Constructive Feedforward Networks with Random Hidden Nodes (IEEE Transactions on Neural Networks)
2. A generalized growing and pruning RBF (GGAP-RBF) neural network for function approximation (IEEE Transactions on Neural Networks)
3. Making best use of model evaluations to compute sensitivity indices
4. Enhanced random search based incremental extreme learning machine (Neurocomputing)
5. Error Minimized Extreme Learning Machine With Growth of Hidden Nodes and Incremental Learning (IEEE Transactions on Neural Networks)
6. Second Order Derivatives for Network Pruning: Optimal Brain Surgeon
7. Multilayer feedforward networks are universal approximators
8. Sensitivity analysis for chemical models. (Chemical Reviews)
9. Extreme learning machine: a new learning scheme of feedforward neural networks (2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541))
10. Modelling with constructive backpropagation

Their papers

• A structure optimisation algorithm for feedforward neural network construction (2013) — 76 citations