Amber Young

Artificial Intelligence · 2019

A researcher in the Science corpus: 1 paper; trusts 2 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Amber Young trusts overall

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

  1. D. R. Cox
  2. Tianqi Chen

Papers from Amber Young's perspective

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

  1. Greedy function approximation: A gradient boosting machine.
  2. XGBoost: A Scalable Tree Boosting System
  3. A working guide to boosted regression trees.
  4. Prevalence of cardiovascular disease in type 2 diabetes: a systematic literature review of scientific evidence from across the world in 2007–2017 (Cardiovascular Diabetology)
  5. Application of support vector machine modeling for prediction of common diseases: the case of diabetes and pre-diabetes
  6. Medical groups' adoption of electronic health records and information systems. (Health Affairs)
  7. The Regression Analysis of Binary Sequences
  8. Machine learning applications in cancer prognosis and prediction
  9. Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes.
  10. American Heart Association. (P & T : a peer-reviewed journal for formulary management)

Their papers