To solve the problem that support vector machine(SVM) can only classify the small samples set, a new algorithm which applied SVM to density clustering is proposed.
为了解决支持向量机的分类仅应用于较小样本集的问题，提出了一种密度聚类与支持向量机相结合的分类算法。
The way of fault Diagnoses based on Support Vector Machine has a simple model compared with the traditional method. It also has great ability to classify, and the best generalization.
与传统的故障诊断方法相比，基于支持向量机的故障诊断方法具有模型简单、分类能力强、推广能力好等特点。
Then, support vector machine is selected to classify aiming at clinical ECG data. Finally, classifications combination approach is analyzed.
随后针对实际的临床十二导联心电图数据，实验了两种支持向量机的分类方法。
This algorithm, in the incremental study question, is more effective than the traditional support vector machine, with assuring the classify accuracy.
本算法在保证分类准确度的同时，在增量学习问题上比传统的支持向量机有效。
This paper proposes an algorithm of shot boundary detection, which employs Support Vector Machine (SVM) to classify visual attention features based on the research results of psychology.
借鉴心理学中有关视觉注意的研究成果，提出了一种采用符合人类视觉注意的特征，并利用支持矢量机进行视频镜头边界检测的算法。
This paper proposes an algorithm of shot boundary detection, which employs Support Vector Machine (SVM) to classify visual attention features based on the research results of psychology.
借鉴心理学中有关视觉注意的研究成果，提出了一种采用符合人类视觉注意的特征，并利用支持矢量机进行视频镜头边界检测的算法。
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