关键词: 归一化, 特征向量, 参数优化, 支持向量机

Abstract: Feature extraction and the parameter optimization of classifiers are two key methods for the improvement of the classification accuracy.The paper uses normalization method for the feature transformation based on the public database UCI.KNN，PNN and SVM are employed for classification.The effects of normalization on the accuracy of classification and parameter optimization are discussed.The results of experiment show normalization improved effectively the accuracies of classifiers，especially for SVM，reduce the searching range of the parameters of classifiers and the training periods.

Key words: normalization, feature vector, parameter optimization, Support Vector Machine（SVM）