The nature of statistical learning theory
The nature of statistical learning theory
Support vector domain description
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Editorial: Geocomputation of mineral exploration targets
Computers & Geosciences
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In this contribution, we describe an application of support vector machine (SVM), a supervised learning algorithm, to mineral prospectivity mapping. The free R package e1071 is used to construct a SVM with sigmoid kernel function to map prospectivity for Au deposits in western Meguma Terrain of Nova Scotia (Canada). The SVM classification accuracies of 'deposit' are 100%, and the SVM classification accuracies of the 'non-deposit' are greater than 85%. The SVM classifications of mineral prospectivity have 5-9% lower total errors, 13-14% higher false-positive errors and 25-30% lower false-negative errors compared to those of the WofE prediction. The prospective target areas predicted by both SVM and WofE reflect, nonetheless, controls of Au deposit occurrence in the study area by NE-SW trending anticlines and contact zones between Goldenville and Halifax Formations. The results of the study indicate the usefulness of SVM as a tool for predictive mapping of mineral prospectivity.