A way of face detection integrating image gray entropy features and SVM classifier is presented.
提出了一种基于支持向量机并综合图像灰度值和灰度熵的人脸检测方法。
Experimental results show that confidence measures and the SVM classifier are effective for utterance verification.
实验结果表明，所提出的置信特征和支持向量机分类器取得了很好的确认效果。
Experimental results show that the constructed kernels used with an SVM classifier perform better than the existing sequence-based methods.
实验结果表明使用我们构造的核函数的支持向量机可以取得比现有的基于序列的方法更好的结果。
K-neighbor classifier, BP neural network classifier and SVM classifier are used for gait identification respectively, and all of them obtain good identification results.
分别采用K -近邻分类器、BP神经网络分类器和SVM分类器进行步态鉴别实验，均获得了较好的鉴别效果。
The experimental results show that the KDDA features achieve the best recognition performance, and the SVM classifier outperforms the minimum Euclidean distance classifier.
结果表明，SVM分类器的识别性能优于最小欧氏距离分类器，且KDDA特征的识别性能最优。
Experiment results show that good effect of defect recognition is achieved using our SVM classifier, and the request of high real time in automated recognition is satisfied.
实验结果表明，使用训练获得的支持向量机分类器识别裂纹缺陷取得了很好的效果，能够满足自动检测的高实时性要求。
Then we train SVM classifier using visual features such as color, texture, and shape. Experimental results show that this approach can improve precision and recall effectively.
该算法和传统色彩量化算法相比，降低了特征向量的维数，计算量小，受光照强度影响较小，提高了检索的性能。
The LS-SVM classifier is adopted, which replaces inequality constraints in SVM by equality constraints. So the computation consumption is reduced and the learning performance is improved.
并且采用了最小二乘支持向量机，用等式约束取代了支持向量机中的不等式约束，降低了运算量，提高了学习效率。
A new method of fault classification for mechanical system by means of support vector machine (SVM) is proposed and a multi-class SVM classifier based on binary classification was developed.
提出了一种利用支持向量机(SVM)对机械系统故障进行分类的新方法；以二值分类为基础，开发了基于支持向量机的多值分类器。
A new method of fault classification for mechanical system by means of support vector machine (SVM) is proposed and a multi-class SVM classifier based on binary classification was developed.
提出了一种利用支持向量机(SVM)对机械系统故障进行分类的新方法；以二值分类为基础，开发了基于支持向量机的多值分类器。
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