Title :
An information-geometrical approach to kernel construction in SVM and its application in soft-sensor modeling
Author :
An, Wen-Sen ; Sun, Yan-Guang
Author_Institution :
Dept. of Autom., Univ. of Sci. & Technol. of China, China
Abstract :
The method of soft-sensor modeling based on support vector machine is first analysed. Geometry of kernel function is studied from information geometry perspective in view of important influence of kernel´s type on the performance of support vector machine. Then the kernel function is constructed in data-dependent way in order to improve the performance of support vector machine. Simulation results for both artificial data and real application show the effectiveness of the proposed method.
Keywords :
intelligent sensors; learning (artificial intelligence); neural nets; regression analysis; support vector machines; SVM; information geometry; kernel function geometry; soft-sensor modeling; support vector machine; Artificial neural networks; Construction industry; Design automation; Information geometry; Kernel; Metals industry; Solid modeling; Sun; Support vector machine classification; Support vector machines; Information geometry; kernel function; soft-sensor modeling; support vector machine;
Conference_Titel :
Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on
Conference_Location :
Guangzhou, China
Print_ISBN :
0-7803-9091-1
DOI :
10.1109/ICMLC.2005.1527704
