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)。
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。
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。
In the paper, we use nearest neighbor classifier to carry the experiment through the ORL person face database.
本文实验选用最近邻分类器，并利用OR L人脸数据库进行对比实验。
In the process of researching post-classification comparison method this paper improve K-nearest neighbor classifier and gain better detection result.
在分类结果比较法的研究过程中，针对城区变化检测的特定问题，改进了经典k近邻法，获得了较好的变化检测结果。
At the same time, testing the order by K-nearest neighbor classifier which is also comes from data mining and the result proves the correctness and feasibility of this method.
同时，运用数据挖掘中的K -最临近分类方法对所得出的强弱顺序进行测试，结果表明了这种区分方法的正确性、可行性。
At the same time, testing the order by K-nearest neighbor classifier which is also comes from data mining and the result proves the correctness and feasibility of this method.
同时，运用数据挖掘中的K -最临近分类方法对所得出的强弱顺序进行测试，结果表明了这种区分方法的正确性、可行性。
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