We emphases discussed the nearest neighbor classifier and support vector machine (SVM) based on the statistical study theory.

在分类器的设计上，重点讨论了最近邻分类器和基于统计学习理论的支持向量机(SVM)。

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The experimental comparisons show that this algorithm outperforms traditional KPCA and K-Nearest Neighbor classifier on both feature extraction and classification.

通过实验比对可知该算法效果在特征提取和分类方面均优于传统核主成分分析法以及最近邻分类器。

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Among, the classifier is designed by the nearest neighbor algorithm and trained based on the pulmonary nodules in LIDC as the sample data.

采用最近邻法设计分类器，并以LIDC库中的结节数据作为样本集，使用留一法进行分类器训练。

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