RESEARCH ON DATA NORMALIZATION FOR SVM TRAINING
Tang Rongzhi;Duan Huichuan;Sun Haitao;School of Information Science and Engineering,Shandong Normal University;Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology,Shandong Normal University;Laboratory and Equipment Management Sector,Shandong Normal University;
Data normalization is a necessary training support vector machine( SVM) to the process of data preprocessing. The normalization method commonly used contains [-1, + 1 ],N( 0,1),etc. However,the existing literature has not yet been found on the research of these commonly used normalization methods of scientific basis. This paper carries out research based on empirical experiments on data normalization,training efficiency and model prediction effect of normalization and non-normalization,etc. Standard data set being selected,this paper analyzed the original non-normalized data,data normalized by different method,artificial inverse normalization and optional attribute of the data by SVM training,recorded changes of objective function values with the number of iterations,training time,model test and k-CV performance information,etc. The experimental results show that the normalization method of limiting the data in the conventional range,such as [-0. 5,+ 0. 5]to [-5,+ 5],N( 0,1) ~ N( 0,5) can obtain the best predictive model in the case of short training time. This paper provides a scientific basis for the normalization of SVM data and learning algorithm of general machine.
【CateGory Index】： TP181