The last topic is about a trainable object detection system using at its core a Support Vector Machine classifier.
最后一个主题将讲解支持向量机分类物中核心使用的可训练物件侦测系统。
Then the SAR imagery is classified to low-reflection area, vegetation covered area and built-up area using support vector machine classifier.
然后利用支持向量机对图像进行分类，将SAR图像分为低反射率区域、城市建筑区和植被覆盖区。
Efficient extraction of image texture features are used on the following support vector machine classifier learning and training have a very important role.
图像纹理特征的有效提取对下面所用到的支持向量机分类器来进行学习和训练有非常重要的作用。
According to the features, we finish the vectorization of texts, and then use support vector machine classifier to distinguish texts and filter illegal texts.
根据特征进行文本向量化，再以支持向量机分类器区分文本类型，实现非法文本的过滤。
Now, support vector machine classifier we experienced has been transplanted to remote ECG diagnosis center. The feedback is being waited to improve the application.
目前，我们实验的支持向量机分类方法已经移植到了远程心电诊断中心，将根据反馈不断完善应用效果。
Support vector machine classifier overcome the shortcoming of the present and commonly used pattern-recognition methods, and has improved the recognition rate effectively.
支持向量机分类器克服了当前常用的模式识别方法的缺点，有效提高了识别率。
To combine the attribute reduction algorithm and the incremental training algorithm of support vector machine, a support vector machine classifier based on rough set is constructed.
将属性约简算法和支持向量机增量训练算法相结合，构造基于粗糙集数据预处理的支持向量机分类器。
Then a support vector machine classifier is constructed and applied to ECG classification. Comparing with the classification of ECG by eyes, the classification results is much more precise.
然后基于支持向量机算法构造了支持向量机分类器，将其用于心电图分类，取得了较高的准确率。
Similarly, based on rough set theory to feature-set reduction, in the optimal decision based on the use of the property least squares support vector machine classifier to identify the flow pattern.
同样，基于粗糙集理论对特征集进行约简，在最优决策属性的基础上使用最小二乘支持向量机分类器对流型进行识别。
Furthermore, combined with the nearest distance classifier, the support vector machine (SVM) is used for classification.
然后再以支持向量机(SVM)和最近邻分类法相结合组成分类器进行分类。
For the support vector machine based learning algorithm of classifier, it is very importance for the support vector to be pre-selected.
在基于支撑矢量机的分类器学习算法中，预先选择支撑矢量是非常重要的。
To apply the discriminative classifier in the speaker recognition, the building sequence kernel support vector machine(SVM) becomes the trend in the field.
为了更好地将区分式分类方法应用于说话者确认系统中，构建序列核支持向量机已成为说话人识别领域的研究热点与趋势。
This paper presents the application of a recently-developed pattern classifier called support vector machine(SVM) in expressway incident detection.
文章采用一种新的模式识别技术——支持向量机（SVM），来进行高速公路的事件检测。
From the perspective of bit plane correlation, this algorithm extracts features, USES support vector machine as a classifier to detect LSB matching.
算法从位平面相关性的角度出发提取特征值，使用支持向量机作为分类器，对LSB匹配算法进行隐写分析。
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。
We emphases discussed the nearest neighbor classifier and support vector machine (SVM) based on the statistical study theory.
在分类器的设计上，重点讨论了最近邻分类器和基于统计学习理论的支持向量机(SVM)。
Extract 21-dimensional statistical characteristics of histogram characteristic function (HCF) field, training classifier with the support vector machine (SVM).
提取直方图特征函数(HCF)域21维统计矩特征组成特征向量，用支持向量机(SVM)训练分类器。
On the design of classifier, by introducing support vector machine, Chinese tone recognition rate is improved under low signal noise rate conditions.
在分类器设计方面，通过引入支持矢量机，进一步提高低信噪比下的汉语声调识别率。
Adopts a classifier support vector machine which is a very good training algorithm, which can acquire very good generalization when the training datum are very few.
文中采用的分类器——支持向量机是一种能在训练样本数很少的情况下达到很好分类推广能力的学习算法。
The self-organizing neural network classifier and particle swarm optimization-support vector machine were designed by author in this paper to use as classification method of motor imagery EEG.
其中自组织神经网络分类器和粒子群优化支持向量机是本文新设计的两种运动想象EEG分类方法。
The self-organizing neural network classifier and particle swarm optimization-support vector machine were designed by author in this paper to use as classification method of motor imagery EEG.
其中自组织神经网络分类器和粒子群优化支持向量机是本文新设计的两种运动想象EEG分类方法。
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