Bahram Choubin

Artificial Intelligence · 2019

A researcher in the Science corpus: 3 papers; trusts 4 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 16 citations into it
• Statistics and Probability — 11 citations into it
• Ecological Modeling — 10 citations into it
• Ecology — 8 citations into it
• Nature and Landscape Conservation — 6 citations into it
• Management Science and Operations Research — 5 citations into it

Whom Bahram Choubin trusts overall

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

1. Peter Buhlmann
2. R. P. Anderson
3. Eric R. Ziegel
4. Richard J. Verrall
5. Yoav Freund
6. Andrew Gelman
7. Wei-Yin Loh
8. Wei‐Wen Wu
9. Youngjo Lee
10. Cha Zhang

Who trusts Bahram Choubin overall

1. Amir Mosavi
2. Pınar Öztürk

Papers from Bahram Choubin's perspective

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

1. Generalized Linear Models
2. Random Forests
3. SPLIT SELECTION METHODS FOR CLASSIFICATION TREES
4. Real vs. artefactual absences in species distributions: tests for Oryzomys albigularis (Rodentia: Muridae) in Venezuela
5. Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation
6. A decision-theoretic generalization of on-line learning and an application to boosting
7. Predicting distributions of Mexican birds using ecological niche modelling methods
8. Boosting for high-dimensional linear models
9. Optimization by Simulated Annealing
10. Double hierarchical generalized linear models

Their papers

• Urban flood risk mapping using the GARP and QUEST models: A comparative study of machine learning techniques (2019) — 369 citations
• An ensemble prediction of flood susceptibility using multivariate discriminant analysis, classification and regression trees, and support vector machines. (2019) — 688 citations
• Flash-flood hazard assessment using ensembles and Bayesian-based machine learning models: Application of the simulated annealing feature selection method. (2019) — 304 citations