Chongzhi Wu
Artificial Intelligence · 2019–2021
A researcher in the Science corpus: 3 papers; trusts 8 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 25 citations into it
- Civil and Structural Engineering — 13 citations into it
- Statistics and Probability — 9 citations into it
- Optimization & Training — 7 citations into it
- Safety, Risk, Reliability and Quality — 7 citations into it
- Statistical Learning Theory — 6 citations into it
- Management, Monitoring, Policy and Law — 5 citations into it
Whom Chongzhi Wu trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Leo Breiman
- Jerome H. Friedman
- Wengang Zhang
- A. Goh
- Robert Tibshirani
- Trevor J. Hastie
- Max Kuhn
- Jian Zhou
- Ron Kohavi
- Tzu-Tsung Wong
Who trusts Chongzhi Wu overall
Papers from Chongzhi Wu's perspective
Ordered by how strongly the researchers Chongzhi Wu trusts endorse each paper. Same corpus, a different order for every researcher.
- Bagging Predictors
- Regression Shrinkage and Selection via the Lasso
- A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection
- XGBoost: A Scalable Tree Boosting System
- Multivariate adaptive regression splines
- Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation (Pattern Recognition)
- Wavelet support vector machine (IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics))
- Applied Predictive Modeling
- Error Measures for Generalizing About Forecasting Methods: Empirical Comparisons
- An improvement to MLR model for predicting liquefaction-induced lateral spread using multivariate adaptive regression splines
Their papers
- Prediction of undrained shear strength using extreme gradient boosting and random forest based on Bayesian optimization (2021) — 770 citations
- State-of-the-art review of soft computing applications in underground excavations (2020) — 442 citations
- Assessment of pile drivability using random forest regression and multivariate adaptive regression splines (2019) — 187 citations