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 paper, we use nearest neighbor classifier to carry the experiment through the ORL person face database.

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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)。

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

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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。

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

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为了验证特征的有效性，使用最近邻及概率神经网络分类器进行了目标识别，得到满意的识别率。

In order to validate character validity, use NearestNeighbor (NN) and probabilistic neural network (PNN) classification identify target, gain content identification probability.

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然后再以支持向量机(SVM)和最近邻分类法相结合组成分类器进行分类。

Furthermore, combined with the nearest distance classifier, the support vector machine (SVM) is used for classification.

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本文提出了一种基于最近邻线分类器的新的双端检测器(DTD)。

This paper presents a novel double-talk detector (DTD) based on a nearest neighbor line (NNL) classifier.

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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。

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

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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。

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

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