然后利用支持向量机分类方法对缺陷进行识别。
Wavelet packet Analysis is applied to feature extraction of ultrasonic signals, and the support vector machine is employed to perform the identification task.
该文讨论了支持向量机分类方法应用于植物病斑形状识别。
In this paper, classification method of SVM for shape recognition of plant disease spot was discussed.
支持向量机分类器结构简单、可获得全局最优、泛化能力强。
SVM has good characteristics of simple structure, global optimum and strong generalization ability.
粒子群优化支持向量机分类效果稍好，但是增加了网络的复杂度；
Particle swarm optimization- support vector machine classification has slightly better result than self-organizing neural networks, but the complexity of network was increased.
对于已知行为，采用加权支持向量机分类算法来识别其行为类别；
For the known activities, the weighted support vector machine (WSVM) is used to recognize their types.
结果表明，优化核参数的支持向量机分类器准确率高，实时性好。
The classification accuracies indicated that the optimal kernel-parameter method could get optimal results.
介绍了支持向量机分类和回归算法，将其应用于梁结构的损伤诊断中。
This paper introduces the support vector classification and regression algorithms, which are applied to the structure damage identification.
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。
The last topic is about a trainable object detection system using at its core a Support Vector Machine classifier.
核函数作为样本相似性的衡量尺度是影响支持向量机分类效果的重要因素。
As the similarity metrics of the samples, kernel function is a key factor that affects the classification.
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 confidence measures and the SVM classifier are effective for utterance verification.
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。
Support vector machine classifier overcome the shortcoming of the present and commonly used pattern-recognition methods, and has improved the recognition rate effectively.
针对结构隐式极限状态函数的可靠性分析，提出了一种支持向量机分类迭代算法。
For reliability analysis of structure with implicit limit state function, an iterative algorithm was presented on the basis of support vector classification machine.
针对非线性问题，提出了基于支持向量机分类基础的先分类、再回归的预测方法。
For non-linear problem, the forecasting technique of pre-classification and later regression was proposed, based on the classification approach of Support Vector Machine (SVM).
分析了基于记忆库混沌时间序列预测方法，引入一种改进核函数的支持向量机分类器。
Secondly the prediction technology of chaotic time series is studied based on memory-based predictor.
本文在自己建立小样本人脸库的基础上，对人脸图像进行了支持向量机分类识别的实验。
In this thesis, to establish their own sample libraries on the basis of my face, the face image to a support vector machine classification experiments.
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。
According to the features, we finish the vectorization of texts, and then use support vector machine classifier to distinguish texts and filter illegal texts.
设计了基于听觉原理和支持向量机分类器的风机故障诊断系统，并应用于风机故障诊断。
A fault diagnosis system is designed based on auditory principle and SVM, and is also applied to fault diagnosis for fan.
这一方法大大提高了支持向量机分类的泛化能力，从而大大提高了支持向量机的应用范围。
This approach greatly improves the generalization ability of SVM classification and its application area is extended.
首先利用色度矩提取植物病害叶片的特征向量，然后利用支持向量机分类方法进行病害的识别。
At first extracting features of chromaticity moments was done then classification method of SVM for recognition of plant disease was discussed.
首先利用色度矩提取植物病害叶片的特征向量，然后利用支持向量机分类方法进行病害的识别。
At first, extracting features of chromaticity moments was done, then classification method of SVM for recognition of plant disease was discussed.
在进行分类时，首先以色度矩作为特征向量，然后将支持向量机分类方法应用于黄瓜病害的识别。
At first, extracting features of chromaticity moments is done, then classification method of SVM for recognition of cucumber disease is discussed.
该方法利用大量的未标识数据进行有效聚类，并将聚类结果用于小样本情形下的支持向量机分类。
This approach exploits unlabeled data for efficient clustering, which is applied in the classification with support vector machine (SVM) in the case of small-size training samples.
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。
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.
针对基于基因表达数据的分类，本文从特征基因选择和支持向量机分类算法两个方面进行了改进。
This thesis improves classification using gene expression data method in two aspects: feature selection and SVMs classification algorithm.
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。
Efficient extraction of image texture features are used on the following support vector machine classifier learning and training have a very important role.
为了将一般增量学习算法扩展到并行计算环境中，提出一种基于多支持向量机分类器的增量学习算法。
In order to extend common incremental learning algorithms into a parallel computation setting, an incremental learning algorithm with multiple support vector machine classifiers is proposed.
实验结果表明，基于KPCA特征提取法的支持向量机分类器的分类错误率在这四种分类算法中最低。
The experiment results concludes that the SVM classification method based on KPCA have the better classification effect than the other three.
实验结果表明，基于KPCA特征提取法的支持向量机分类器的分类错误率在这四种分类算法中最低。
The experiment results concludes that the SVM classification method based on KPCA have the better classification effect than the other three.
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