Hai-Jun Yang

2004

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

Whom Hai-Jun Yang trusts overall

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

1. Jerome H. Friedman
2. Yoav Freund
3. Robert E. Schapire

Who trusts Hai-Jun Yang overall

1. Anna Veronika Dorogush
2. Polina Kirichenko

Papers from Hai-Jun Yang's perspective

Ordered by how strongly the researchers Hai-Jun Yang trusts endorse each paper. Same corpus, a different order for every researcher.

1. Greedy function approximation: A gradient boosting machine.
2. Classification and regression trees
3. Experiments with a New Boosting Algorithm
4. The Boosting Approach to Machine Learning An Overview
5. A Short Introduction to Boosting
6. Game theory, on-line prediction and boosting
7. Bias, Variance , And Arcing Classifiers
8. The Strength of Weak Learnability
9. A theory of the learnable
10. UCI Repository of machine learning databases

Their papers

• Boosted decision trees as an alternative to artificial neural networks for particle identification (2004) — 600 citations