Richard A. Bauder

Artificial Intelligence · 2018

A researcher in the Science corpus: 1 paper; trusts 13 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 22 citations into it
• Electrical and Electronic Engineering — 8 citations into it

Whom Richard A. Bauder trusts overall

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

1. Jerome H. Friedman
2. Taghi M. Khoshgoftaar
3. Jason Van Hulse
4. Nitesh V. Chawla
5. Andrew Y. Ng
6. Andrew McCallum
7. Salvador Garcia

Papers from Richard A. Bauder's perspective

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

1. SMOTE: Synthetic Minority Over-sampling Technique
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Gradient-based learning applied to document recognition
4. Greedy function approximation: A gradient boosting machine.
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Classification of Imbalanced Data: a Review (International journal of pattern recognition and artificial intelligence)
7. SMOTEBoost: Improving Prediction of the Minority Class in Boosting
8. ImageNet classification with deep convolutional neural networks
9. SVMs Modeling for Highly Imbalanced Classification
10. On Discriminative vs. Generative Classifiers: A comparison of logistic regression and naive Bayes

Their papers

• A survey on addressing high-class imbalance in big data (2018) — 675 citations