Geochemical Fingerprinting of Coltan Ores by Machine Learning on Uneven Datasets

Two modern machine learning techniques, Linear Programming Boosting and Support Vector Machines, are introduced and applied to a geochemical dataset of niobium–tantalum (“coltan”) ores from Central Africa to demonstrate how such information may be used to distinguish ore provenance, i.e., place of origin.