Extracting mineralized information based on ACO_SVM

Tuning the hyperparameters of Support Vector Machine (SVM) is an important way to improve the generalization performance of SVM. Because it is time consuming to seek for optimal parameters by grid search method, the Ant Colony Optimization (ACO) search method selecting parameters is presented in my paper, which can acquire the best parameters of SVM‥ My research was done in Xijieri area, Qinghai province. Firstly, analyzed the behavior of SVM is analyzed that have influence on the classrate; Secondly, ACO algorithm is used for is designed and implemented, which is used for seeking for optimal SVM parameters; In the end, mineralized information is extracted by ACO_SVM. The experimental results show that ACO algorithm can seek for parameters quicklier and more satisfactorily than grid search method; Through on the spot investigation and comparing with data of the known alteration areas in the mineral geology mapping of field, we find that extracting mineralized information by ACO_SVM is a good way.