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support vector machine tree 支持向量机树
Wavelet and support vector machine are used to classify the audio clips. 使用小波变换和支持向量机的方法对音频进行分类。
The uploaded Support Vector Machine (SVM) Classifier can classify text-type data well. 详细说明:支持向量机分类器(可分类文本,编的非常不错)
Support Vector Machine (SVM) , which based on Statistic Learning Theory, has the adaptive generation ability. 建立在统计学习理论基础之上的支持向量机具有和样本数相适应的最优泛化能力。
For this reason, several important properties of kernel function are discussed on the basis of support vector machine. 为此，在研究支持向量机的基础上，给出了核函数的若干重要性质。
The support vector machine forecasting model is proposed to improve the accuracy. 为提高精度，提出了支持向量机非线性的组合预测模型。
Handwritten number recognition based on support vector machine adopts the US post service database. 支持向量机的手写体数字识别中,采用美国邮政服务数据库。
NPA is currently one of the best training algorithm for support vector machine classifier. 支撑向量机是90年代中期发展起来的机器学习技术,NPA算法是目前最优秀的学习算法之一。
Finally, Support Vector Machine (SVM) was used to validate the correctness of algorithm. 最后利用支持向量机(SVM)作为算法验证的分类器。
A multiclass support vector machine suitable for short duration power quality disturbance is designed. 设计了适用于短时电能质量扰动的支持向量机多值分类器。
A new method of dynamic clustering based on the idea of optimal hypersphere and support vector machine. 摘要提出了基于最优超球面与支持向量机思想的动态聚类算法。
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。
A new time series prediction method was put forward, based on fuzzy least square support vector machine (FLS-SVM). 摘要提出了一种基于隶属度模糊最小二乘支持向量机(FLS-SVM)的时间序列预测新方法。
Then, using support vector machine classifier to conduct quality classification on speed ratio control valve. 然后，使用支持向量机分类器对速比控制阀进行质量分类。
The theory of least square support vector machine was introduced.The LS-SVM model was set up to predict PMV. 摘要介绍了一种新型的机器学习算法-最小二乘支持向量机的原理，并针对预测PMV指标建立了最小二乘支持向量机预测模型。
In this paper,a fuzzy hypersphere support vector machine(FHS-SVM) landmine detector was proposed. 本文提出了模糊超球面支持向量机(FHS-SVM)地雷检测器。
Support Vector Machine (SVM) is a novel powerful machine learning method based on SLT. 支持向量机是在该理论体系下产生的一种新的、非常有力的机器学习方法。
The hyperplane revises the error of the standard Support Vector Machines. 该分类面校正了标准的支持向量机的分类误差。
On this base,a novel segmentation algorithm using support vector machine is presented for segmentation. 在此基础上,探索性地将支持向量机用于骨髓细胞图像的分割。
Experimental results show that support vector machine approach is a promising technique for image segmentation. 实验结果表明，支持向量机方法是一种很有前景的图像分割技术。