Mario Chica-Olmo

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; #4,253 of 10,717 by trust here, #3,363 by citations
• Media Technology — 25 citations into it; #1,861 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; #5,089 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 Mario Chica-Olmo trusts overall

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

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

Who trusts Mario Chica-Olmo overall

1. Quanlong Feng
2. Jiantao Liu
3. Liheng Zhong
4. Abdulhakim M. Abdi
5. Zhen Zhen

Papers from Mario Chica-Olmo's perspective

Ordered by how strongly the researchers Mario Chica-Olmo trusts endorse each paper. Same corpus, a different order for every researcher.

1. Bagging Predictors
2. An Introduction to Variable and Feature Selection
3. Investigation of the random forest framework for classification of hyperspectral data (IEEE Transactions on Geoscience and Remote Sensing)
4. Random forest classifier for remote sensing classification
5. Toward an Optimal SVM Classification System for Hyperspectral Remote Sensing Images (IEEE Transactions on Geoscience and Remote Sensing)
6. Random Search for Hyper-Parameter Optimization
7. Classification and regression trees
8. Retrieving forest structure variables based on image texture analysis and IKONOS-2 imagery
9. An assessment of the effectiveness of decision tree methods for land cover classification
10. Geochemical Anomaly and Mineral Prospectivity Mapping in Gis

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