Can Huang

Artificial Intelligence · 2017–2018

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

Subfields

• Artificial Intelligence — 5 citations into it

Whom Can Huang trusts overall

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

1. Jerome H. Friedman

Papers from Can Huang's perspective

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

1. Bagging Predictors
2. Greedy function approximation: A gradient boosting machine.
3. Random Forests
4. XGBoost: A Scalable Tree Boosting System
5. Scikit-learn: Machine Learning in Python
6. Robust and Accurate Shape Model Matching Using Random Forest Regression-Voting (IEEE Transactions on Pattern Analysis and Machine Intelligence)
7. Fault-Tolerant Control of Wind Turbines: A Benchmark Model (IEEE Transactions on Control Systems Technology)
8. Variable Selection Using Random Forests
9. FAST User's Guide
10. Stochastic gradient boosting

Their papers

• Present situation and future prospect of renewable energy in China (2017) — 496 citations
• A Data-Driven Design for Fault Detection of Wind Turbines Using Random Forests and XGboost (2018) — 476 citations