Masoud Sohrabi

Management, Monitoring, Policy and Law · 2019–2020

A researcher in the Science corpus: 2 papers; trusts 2 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Management, Monitoring, Policy and Law — 24 citations into it
• Global and Planetary Change — 20 citations into it

Whom Masoud Sohrabi trusts overall

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

1. Jerome H. Friedman
2. Leo Breiman
3. Graham J. Williams

Papers from Masoud Sohrabi's perspective

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

1. Greedy function approximation: A gradient boosting machine.
2. Arcing classifier (with discussion and a rejoinder by the author)
3. Data Mining with Rattle and R: The Art of Excavating Data for Knowledge Discovery
4. A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS (Computational Geosciences)
5. The Nature of Statistical Learning Theory
6. Bagging Predictors
7. Consistent Nonparametric Regression
8. Game theory, on-line prediction and boosting
9. Bias, Variance , And Arcing Classifiers
10. Shape Quantization and Recognition with Randomized Trees

Their papers

• An ensemble model for landslide susceptibility mapping in a forested area (2020) — 74 citations
• GIS-based landslide susceptibility mapping using numerical risk factor bivariate model and its ensemble with linear multivariate regression and boosted regression tree algorithms (2019) — 127 citations