Edurne Barrenechea Tartas
Artificial Intelligence · 2011–2016
A researcher in the Science corpus: 4 papers; trusts 36 researchers and is trusted by 6 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 99 citations into it; #17,243 of 35,345 by trust here, #8,077 by citations
- Statistics and Probability — 36 citations into it
- Management Science and Operations Research — 26 citations into it
- Information Systems — 18 citations into it; #6,133 of 8,771 by trust here, #5,942 by citations
- Computer Vision and Pattern Recognition — 17 citations into it
- Computational Theory and Mathematics — 12 citations into it
- Electrical and Electronic Engineering — 12 citations into it; #14,609 of 16,539 by trust here, #5,738 by citations
- Evaluation & Benchmarks — 9 citations into it; #2,858 of 3,147 by trust here, #1,542 by citations
Whom Edurne Barrenechea Tartas trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Salvador Garcia
- Jerry M. Mendel
- Francisco Herrera
- Jesús Alcalá-Fdez
- Jaka Demsar
- Ronald R. Yager
- Nitesh V. Chawla
- Alberto Fernández
- Rosa M. Rodríguez
- Ludmila I. Kuncheva
Who trusts Edurne Barrenechea Tartas overall
Papers from Edurne Barrenechea Tartas's perspective
Ordered by how strongly the researchers Edurne Barrenechea Tartas trusts endorse each paper. Same corpus, a different order for every researcher.
- Gradient-based learning applied to document recognition
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- A decision-theoretic generalization of on-line learning and an application to boosting
- Statistical Learning Theory
- ImageNet classification with deep convolutional neural networks
- Bagging Predictors
- Fast Effective Rule Induction
- SMOTE: Synthetic Minority Over-sampling Technique
- Going deeper with convolutions
Their papers
- A Historical Account of Types of Fuzzy Sets and Their Relationships (2016) — 424 citations
- A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Based Approaches (2012) — 1,775 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