Classification of Metallogenic Favourability Degree Using Support Vector Machines

Abstract

Support vector machines (SVMs) have become very popular as methods for learning from examples, which are powerful tools used to solve the problem characterized by small sample, nonlinearity, and high dimension with a good generalization performance based on structural risk minimization. The paper discusses the support vector classification algorithm in some detail and describes a SVMs based-system that learns from examples to classify metallogenic probability of copper ore. The experimental results show that support vector classification has high recognition rates and good generalization performance for small sample and suggest that SVMs are promising methods for classification of metallogenicl favourability degree.