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《JOURNAL OF COMPUTER RESEARCH AND DEVELOPMENT》 2000-01
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DATABASE LEARNING ALGORITHMS BASED ON SUPPORT VECTOR MACHINE

TIAN Sheng Feng and HUANG Hou Kuan(Department of Computer Science and Technology, Northern Jiaotong University, Beijing 100044)  
A decision making method using large amount of data in databases is introduced in this paper. For the problems concerning a part of data in a database only, the support vector machine with high generalization ability is adopted to learn classification rule and regression function from the relative data to current problem in the database, in order to perform the classification and evaluation tasks. In the support vector machine, the data are mapped into a high dimensional feature space with a nonlinear mapping, in which linear classification and regression are performed. This is a convex quadratic optimization problem. With the above algorithms introduced, a tunnel engineering supporting design system is realized and a good result is obtained.
【CateGory Index】: TP18
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