This paper put forward and carried out a text classification method using feed-forward neural network and K-nearest neighbor.

提出并实现了一种结合前馈型神经网络和K最近邻的文本分类算法。

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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。

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This paper presents a fast text classification algorithm based on KNN (K Nearest Neighbor).

提出了一种基于K近邻(KNN)原理的快速文本分类算法。

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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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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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