Mohammad Asadpour

Artificial Intelligence · 2020

A researcher in the Science corpus: 1 paper; trusts 3 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 23 citations into it

Whom Mohammad Asadpour trusts overall

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

1. Robert E. Schapire
2. Leo Breiman

Papers from Mohammad Asadpour's perspective

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

1. A decision-theoretic generalization of on-line learning and an application to boosting
2. Bagging Predictors
3. Statistical Comparisons of Classifiers over Multiple Data Sets
4. Greedy function approximation: A gradient boosting machine.
5. XGBoost: A Scalable Tree Boosting System
6. BoosTexter: A Boosting-based System for Text Categorization (Machine-mediated learning)
7. Using AUC and accuracy in evaluating learning algorithms (IEEE Transactions on Knowledge and Data Engineering)
8. A study of the behavior of several methods for balancing machine learning training data
9. Ensemble-based classifiers
10. A Brief Introduction to Boosting

Their papers

• Boosting methods for multi-class imbalanced data classification: an experimental review (2020) — 422 citations