Lucas B. V. de Amorim

Artificial Intelligence · 2022

A researcher in the Science corpus: 1 paper; trusts 14 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 13 citations into it
• Computer Vision and Pattern Recognition — 5 citations into it

Whom Lucas B. V. de Amorim trusts overall

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

1. Leo Breiman
2. Jerome H. Friedman
3. Ludmila I. Kuncheva
4. Niko Speybroeck
5. Subhash C. Bagui
6. Yoav Freund
7. Thomas M. Cover
8. Tianqi Chen
9. Trevor J. Hastie
10. Rafael M. O. Cruz

Papers from Lucas B. V. de Amorim's perspective

Ordered by how strongly the researchers Lucas B. V. de Amorim 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. A decision-theoretic generalization of on-line learning and an application to boosting
5. Scikit-learn: Machine Learning in Python
6. Nearest neighbor pattern classification
7. ImageNet classification with deep convolutional neural networks
8. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
9. XGBoost: A Scalable Tree Boosting System
10. Gradient-based learning applied to document recognition

Their papers

• The choice of scaling technique matters for classification performance (2022) — 371 citations