Mikel Galar

Artificial Intelligence · 2011–2018

A researcher in the Science corpus: 5 papers; trusts 42 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 105 citations into it; #13,092 of 35,345 by trust here, #6,911 by citations
• Statistics and Probability — 24 citations into it
• Information Systems — 23 citations into it; #6,134 of 8,771 by trust here, #5,942 by citations
• Electrical and Electronic Engineering — 22 citations into it; #9,927 of 16,539 by trust here, #4,925 by citations
• Computer Vision and Pattern Recognition — 14 citations into it
• Accounting — 11 citations into it; #8,034 of 8,970 by trust here, #5,847 by citations
• Evaluation & Benchmarks — 11 citations into it; #2,859 of 3,147 by trust here, #1,542 by citations
• Optimization & Training — 10 citations into it; #2,509 of 2,601 by trust here, #1,072 by citations

Whom Mikel Galar trusts overall

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

1. Salvador Garcia
2. Jaka Demsar
3. Nitesh V. Chawla
4. Jesús Alcalá-Fdez
5. Alberto Fernández
6. Francisco Herrera
7. Leo Breiman
8. Ludmila I. Kuncheva
9. Yoav Freund
10. Gustavo E. A. P. A. Batista

Who trusts Mikel Galar overall

1. Francisco Charte
2. Omer Sagi
3. Francisco Herrera
4. Verónica Bolón-Canedo
5. Lior Rokach
6. Alberto Fernández
7. Bartosz Krawczyk
8. Antonio Jesús Rivera
9. Sara Fotouhi
10. Jafar Tanha

Papers from Mikel Galar's perspective

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

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. Gradient-based learning applied to document recognition
3. ImageNet classification with deep convolutional neural networks
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Going deeper with convolutions
10. GENERATIVE ADVERSARIAL NETS

Their papers

• A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Based Approaches (2012) — 1,775 citations
• Analysing the classification of imbalanced data-sets with multiple classes: Binarization techniques and ad-hoc approaches (2013) — 390 citations
• Learning from Imbalanced Data Sets (2018) — 1,073 citations
• EUSBoost: Enhancing ensembles for highly imbalanced data-sets by evolutionary undersampling (2013) — 399 citations
• An overview of ensemble methods for binary classifiers in multi-class problems: Experimental study on one-vs-one and one-vs-all schemes (2011) — 779 citations