Milton García-Borroto
Artificial Intelligence · 2010–2019
A researcher in the Science corpus: 9 papers; trusts 20 researchers and is trusted by 9 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 79 citations into it; #11,970 of 35,345 by trust here, #16,771 by citations
- Information Systems — 26 citations into it; #5,301 of 8,771 by trust here, #5,713 by citations
- Evaluation & Benchmarks — 21 citations into it
- Computational Theory and Mathematics — 18 citations into it; #10,141 of 12,182 by trust here, #11,462 by citations
- Electrical and Electronic Engineering — 16 citations into it; #10,445 of 16,539 by trust here, #11,313 by citations
- Optimization & Training — 13 citations into it
- Statistics and Probability — 13 citations into it
- Statistical Learning Theory — 9 citations into it
Whom Milton García-Borroto trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Thomas G. Dietterich
- J. Ross Quinlan
- Tin Kam Ho
- Corinna Cortes
- Usama M. Fayyad
- Jaka Demsar
- Vladimir Naumovich Vapnik
- Salvador Garcia
- Guozhu Dong
- Leo Breiman
Who trusts Milton García-Borroto overall
- Octavio Loyola-González
- Raúl Monroy
- José Francisco Martínez Trinidad
- J. A. Carrasco-Ochoa
- Guozhu Dong
- Jorge Rodríguez
Papers from Milton García-Borroto's perspective
Ordered by how strongly the researchers Milton García-Borroto trusts endorse each paper. Same corpus, a different order for every researcher.
- UCI Repository of machine learning databases
- Programs for Machine Learning
- ImageNet classification with deep convolutional neural networks
- Gradient-based learning applied to document recognition
- The Nature of Statistical Learning Theory
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- SMOTE: Synthetic Minority Over-sampling Technique
- A study of the behavior of several methods for balancing machine learning training data
- Fast Training of Support Vector Machines using Sequential Minimal Optimization
Their papers
- Study of the impact of resampling methods for contrast pattern based classifiers in imbalanced databases (2016) — 173 citations
- PBC4cip: A new contrast pattern-based classifier for class imbalance problems (2017) — 73 citations
- Fuzzy emerging patterns for classifying hard domains (2011) — 39 citations
- A Pattern-Based Approach for Detecting Pneumatic Failures on Temporary Immersion Bioreactors (2019) — 12 citations
- Proactive Forest for Supervised Classification (2018) — 3 citations
- Effect of class imbalance on quality measures for contrast patterns: An experimental study (2016) — 17 citations
- Detecting Pneumatic Failures on Temporary Immersion Bioreactors (2016) — 3 citations
- Finding the best diversity generation procedures for mining contrast patterns (2015) — 31 citations
- A New Emerging Pattern Mining Algorithm and Its Application in Supervised Classification (2010) — 18 citations