Support vector machine: A tool for mapping mineral prospectivity

By Renguang Zuo, Emmanuel John Muico Carranza. Computational Geosciences, 2011

478 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Mechanical Engineering, Media Technology

View Paper: https://www.semanticscholar.org/paper/22977101b4d949921e6f827260004ff345354e5d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Alice G. Laborte
2. Emmanuel John Muico Carranza
3. Renguang Zuo

In Mechanical Engineering

1. Emmanuel John Muico Carranza

In Media Technology

1. D. Abaye
2. Joseph Otoo.
3. Emmanuel John Muico Carranza
4. Ernest Yeboah Boateng

Citations in the corpus

• Basic Tenets of Classification Algorithms K-Nearest-Neighbor, Support Vector Machine, Random Forest and Neural Network: A Review (2020, background): “Thus far, there has been no classification method that works best on any given problem [26].”
• Random forest predictive modeling of mineral prospectivity with small number of prospects and data with missing values in Abra (Philippines) (2015, background): “The marginal effect is, therefore, akin to bivariate measures of spatial associations between target and predictor variables.”