The results indicate that the performance of ensembles of KFDA is better than that of FDA, PCA and pre-classifier. The prediction accuracy is about 86.5%.

实验结果表明，使用组合KFDA的方法预测的效果优于FDA和PCA以及单个KFDA分类器的预测效果，预测准确率为86.5%。

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In order to improve the detection rate and reduce the training time, KFDA-SVM intrusion detection technology is proposed which combines the feature extraction technology and classification algorithm.

为了提高分类正确率和减少训练时间，将特征抽取技术与分类算法结合，提出了一种基于KFDA - SVM的入侵检测技术。

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In order to improve the detection rate and reduce the training time, KFDA-SVM intrusion detection technology is proposed which combines the feature extraction technology and classification algorithm.

为了提高分类正确率和减少训练时间，将特征抽取技术与分类算法结合，提出了一种基于KFDA - SVM的入侵检测技术。

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