M. Kocher

Artificial Intelligence · 2016

A researcher in the Science corpus: 1 paper; trusts 9 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 15 citations into it
• Statistics and Probability — 12 citations into it

Whom M. Kocher trusts overall

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

1. Leo Breiman
2. Nitesh V. Chawla
3. Eric R. Ziegel
4. S. Geisser
5. Chao Chen
6. Christopher Zorn
7. Gary King

Papers from M. Kocher's perspective

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

1. The Elements of Statistical Learning
2. SMOTE: Synthetic Minority Over-sampling Technique
3. Random Forests
4. The Predictive Sample Reuse Method with Applications
5. Improving Quantitative Studies of International Conflict: A Conjecture
6. An introduction to ROC analysis
7. SMOTEBoost: Improving Prediction of the Minority Class in Boosting
8. Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author)
9. Sensitivity Analysis of Empirical Results on Civil War Onset
10. Amelia II: A Program for Missing Data

Their papers

• Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data (2016) — 244 citations