An kernel clustering intrusion detection approach based on outlier detection is presented in this paper.

提出了一种基于孤立点检测的核聚类入侵检测方法。

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In this paper, an SVM-based approach applied to predict steel quenching degree is presented, and the effects of selecting kernel function on SVM modeling are also analyzed.

本文提出了基于支持向量机模型预测钢淬透性的方法，并分析了核函数的选择对支持向量机建模的影响。

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This paper investigates the segmentation of multi-target image based on SVM approach combining feature extraction of kernel PCA.

实验结果表明，结合核主成份分析的特征提取，支持向量机方法是一种很有前景的多目标图像分割技术。

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