John W. Coulston

Artificial Intelligence · 2011–2016

A researcher in the Science corpus: 2 papers; trusts 3 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 8 citations into it
• Statistics and Probability — 6 citations into it

Whom John W. Coulston trusts overall

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

1. Jerome H. Friedman
2. Greg Ridgeway
3. Trevor J. Hastie

Papers from John W. Coulston's perspective

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

1. Stochastic gradient boosting
2. Greedy function approximation: A gradient boosting machine.
3. Random Forests
4. Special Invited Paper-Additive logistic regression: A statistical view of boosting
5. CLASSIFICATION AND REGRESSION TREES: A POWERFUL YET SIMPLE TECHNIQUE FOR ECOLOGICAL DATA ANALYSIS
6. Newer Classification and Regression Tree Techniques: Bagging and Random Forests for Ecological Prediction (Ecosystems)
7. Classification of remotely sensed imagery using stochastic gradient boosting as a refinement of classification tree analysis
8. Using satellite imagery as ancillary data for increasing the precision of estimates for the Forest Inventory and Analysis program of the USDA Forest Service
9. A working guide to boosted regression trees.
10. Reply to Comment on ‘A first map of tropical Africa’s above-ground biomass derived from satellite imagery’ (Environmental Research Letters)

Their papers

• Random forests and stochastic gradient boosting for predicting tree canopy cover: Comparing tuning processes and model performance (2016) — 203 citations
• Post-stratified estimation: with-in strata and total sample size recommendations (2011) — 55 citations