Victor Francisco Rodriguez-Galiano

Ecology · 2012–2015

A researcher in the Science corpus: 3 papers; trusts 11 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Ecology — 26 citations into it; #4,252 of 10,717 by trust here, #3,363 by citations
• Media Technology — 25 citations into it; #1,860 of 3,621 by trust here, #678 by citations
• Artificial Intelligence — 18 citations into it
• Environmental Engineering — 13 citations into it
• Global and Planetary Change — 11 citations into it; #5,088 of 10,225 by trust here, #3,540 by citations
• Atmospheric Science — 10 citations into it
• Evaluation & Benchmarks — 7 citations into it
• Statistical Learning Theory — 5 citations into it

Whom Victor Francisco Rodriguez-Galiano trusts overall

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

1. Leo Breiman
2. Mahesh Pal
3. Emmanuel John Muico Carranza
4. Li Guo
5. Yakoub Bazi
6. DJ Booker
7. Trevor J. Hastie
8. François Kayitakire
9. Miguel Angel Castillo-Santiago
10. Nesrine Chehata

Who trusts Victor Francisco Rodriguez-Galiano overall

1. Quanlong Feng
2. Abdulhakim M. Abdi
3. Jiantao Liu
4. Muhammad Waseem Ahmad
5. Jianhua Gong
6. Aaron E. Maxwell
7. Liheng Zhong
8. Ernest Yeboah Boateng
9. Michal Wozniak
10. John Hogland

Papers from Victor Francisco Rodriguez-Galiano's perspective

Ordered by how strongly the researchers Victor Francisco Rodriguez-Galiano trusts endorse each paper. Same corpus, a different order for every researcher.

1. Bagging Predictors
2. ImageNet classification with deep convolutional neural networks
3. Random Forests
4. An Introduction to Variable and Feature Selection
5. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
6. The Nature of Statistical Learning Theory
7. Investigation of the random forest framework for classification of hyperspectral data (IEEE Transactions on Geoscience and Remote Sensing)
8. Gradient-based learning applied to document recognition
9. ImageNet: A large-scale hierarchical image database
10. Random Search for Hyper-Parameter Optimization

Their papers

• Machine learning predictive models for mineral prospectivity: an evaluation of neural networks, random forest, regression trees and support vector machines (2015) — 1,398 citations
• An assessment of the effectiveness of a random forest classifier for land-cover classification (2012) — 2,705 citations
• Random Forest classification of Mediterranean land cover using multi-seasonal imagery and multi-seasonal texture (2012) — 565 citations