SVM based ore grade valuation model construction

Author

Li, Cuiping ; Li, Juan ; Li, Zhongxue ; Sun, Enji

Author_Institution

Sch. of Civil & Environ. Eng., Univ. of Sci. & Technol., Beijing, China

Volume

2

fYear

2010

fDate

10-12 Aug. 2010

Firstpage

932

Lastpage

935

Abstract

Considering the excellent convex optimization property and the good ability of generalization of support vector machines (SVM), this paper gives out an grade valuation model based on it. Through the processing of data normalization and the optimization analysis of some factor, the SVM based ore grade valuation model is created. The prediction results compare with the Thiessen polygons method, the Distance power inverse ratio method and the Kriging interpolation method, which verifies the feasibility and validity of the SVM based ore grade valuation model.

Keywords

generalisation (artificial intelligence); geophysics computing; interpolation; minerals; support vector machines; Kriging interpolation method; SVM generalization ability; Thiessen polygons; convex optimization property; distance power inverse ratio method; ore grade valuation; support vector machines; valuation model construction; Cost accounting; Interpolation; Kernel; Metals; Ores; Support vector machines; grade; kernel function; support vector machine; valuation;

fLanguage

English

Publisher

ieee

Conference_Titel

Natural Computation (ICNC), 2010 Sixth International Conference on

Conference_Location

Yantai, Shandong

Print_ISBN

978-1-4244-5958-2

Type

conf

DOI

10.1109/ICNC.2010.5583848

Filename

5583848