Robert Verner

Management Science and Operations Research · 2016

A researcher in the Science corpus: 1 paper; trusts 14 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Management Science and Operations Research — 39 citations into it; #9,992 of 11,946 by trust here, #6,888 by citations
• Artificial Intelligence — 33 citations into it; #29,603 of 35,345 by trust here, #25,040 by citations
• Economics and Econometrics — 20 citations into it
• Accounting — 18 citations into it; #7,409 of 8,970 by trust here, #6,570 by citations
• Finance — 17 citations into it
• Time-Series & Forecasting — 11 citations into it
• Electrical and Electronic Engineering — 9 citations into it

Whom Robert Verner trusts overall

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

1. David West
2. S. Mirjalili
3. Esmaeil Hadavandi
4. Tian-Shyug Lee

Who trusts Robert Verner overall

1. Weiwei Jiang
2. Longbing Cao

Papers from Robert Verner's perspective

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

1. Neural networks in business: techniques and applications for the operations researcher (Computers & Operations Research)
2. Stock market prediction of S&P 500 via combination of improved BCO approach and BP neural network
3. Credit scoring using the hybrid neural discriminant technique (Expert systems with applications)
4. Credit rating analysis with support vector machines and neural networks: a market comparative study
5. Using neural networks for forecasting volatility of S&P 500 Index futures prices
6. Neural network applications in finance: A review and analysis of literature (1990-1996)
7. Forecasting stock markets using wavelet transforms and recurrent neural networks: An integrated system based on artificial bee colony algorithm
8. Neural network ensemble strategies for financial decision applications (Computers & Operations Research)
9. Using neural network ensembles for bankruptcy prediction and credit scoring (Expert systems with applications)
10. A two-stage hybrid credit scoring model using artificial neural networks and multivariate adaptive regression splines (Expert systems with applications)

Their papers

• Artificial neural networks in business: Two decades of research (2016) — 313 citations