Elizabeth Thomas

Artificial Intelligence · 2017

A researcher in the Science corpus: 1 paper; trusts 8 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Elizabeth Thomas trusts overall

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

  1. Jerome H. Friedman
  2. Eric R. Ziegel
  3. Tianqi Chen
  4. Gavin C. Cawley
  5. Qingsong Xu

Papers from Elizabeth Thomas's perspective

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

  1. Greedy function approximation: A gradient boosting machine.
  2. The Elements of Statistical Learning
  3. Stochastic gradient boosting
  4. Scikit-learn: Machine Learning in Python
  5. XGBoost: A Scalable Tree Boosting System
  6. Practical Lessons from Predicting Clicks on Ads at Facebook
  7. An empirical comparison of supervised learning algorithms
  8. Monte Carlo cross validation
  9. Where is the semantic system? A critical review and meta-analysis of 120 functional neuroimaging studies. (Cerebral Cortex)
  10. On Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation

Their papers