S. Jalabert

Nature and Landscape Conservation · 2010

A researcher in the Science corpus: 1 paper; trusts 5 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom S. Jalabert trusts overall

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

  1. Jerome H. Friedman
  2. Jane Elith
  3. David Ruppert
  4. Greg Ridgeway
  5. Niko Speybroeck
  6. Rick L. Lawrence
  7. B. Vos
  8. Manuel P. Martin
  9. Catherine H. Graham
  10. J. Leathwick

Papers from S. Jalabert's perspective

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

  1. Greedy function approximation: A gradient boosting machine.
  2. Stochastic gradient boosting
  3. The Elements of Statistical Learning: Data Mining, Inference, and Prediction
  4. Do they? How do they? WHY do they differ? On finding reasons for differing performances of species distribution models
  5. Why do we still use stepwise modelling in ecology and behaviour? (Journal of Animal Ecology)
  6. A working guide to boosted regression trees.
  7. Predictive Quality of Pedotransfer Functions for Estimating Bulk Density of Forest Soils
  8. Classification of remotely sensed imagery using stochastic gradient boosting as a refinement of classification tree analysis
  9. Predicting tree species presence and basal area in Utah: A comparison of stochastic gradient boosting, generalized additive models, and tree-based methods
  10. Extrapolating regional soil landscapes from an existing soil map: Sampling intensity, validation procedures, and integration of spatial context

Their papers