A new short-term load forecasting method is proposed by conjunctive use of Hard C-mean clustering ( HCM) algorithm and support vector machine ( SVM).

In this paper, the estimation method of regression function based on multi-wavelet support vector machine ( WSVM) is put forward.

为此，提出回归函数的小波支持向量机鲁棒估计方法（小波支持向量回归，WSVR，Wavelet Support Vector Regression）。

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A hierarchical method for multi-class digital modulation recognition using support vector machines ( SVM) was developed as a simplified method.

针对这一现状，提出了一种基于支持向量机（SVM）的分级调制识别新方法。

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First, data are pretreated through K-means clustering. Then, attributes are reduced by using rough sets. At last, SVM diagnoses default.

这种方法首先利用K均值聚类对数据进行预处理，然后利用粗糙集对属性进行约简，最后再用支持向量机进行故障诊断。

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A multi-class support vector machine ( SVM) is used to classify the video key frames of scenery documentary.

针对如何从视频关键帧中提取语义的问题，提出了一个使用多类支持向量机（SVM）对风光纪录片的关键帧进行分类来提取语义的方法。

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The paper brings forward a new image recognition method for pathological changes with morphology theory and SVM algorithm.

提出了一种将形态学理论与SVM（支持向量机）算法结合起来的病变图象特征识别方法。

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This paper introduces principles of a novel computer learning method, Support Vector ( Machines,) which has attracted much attention in recent years.

概括介绍了近年来倍受瞩目的一种新的计算机学习方法&支持向量机（Support Vector Machines，简称SVM）方法，这一方法具有坚实的理论基础和出色的应用效果；

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The prediction method of network delays based on support vector machine ( SVM) was put forward.

进而提出了基于支持向量机（SVM）的网络延时预测方法。

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A support vector machine half-supervised machine learning method based on fuzzy C-mean algorithm objective to feature vector automatic acquirement was proposed and used in texture image segmentation.

针对纹理图像分割中的训练样本自动获取问题，提出了一种基于模糊C均值算法的支持向量机半监督图像分割方法。

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A method to realize the P2P flow classification based on the support vector machine is proposed.

与此同时，提出了一种基于支持向量机的P2P流应用级分类方法，利用支持向量机优良的分类性能，对不同应用类型的P2P流进行了分类。

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Support Vector Machine ( SVM) is a hotspot in machine learning field and has been successfully applied in different fields.

支持向量机（support Vector Machine，SVM）是机器学习界的研究热点，并在很多领域都得到了成功的应用。

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In speaker recognition field, recognition method based on Support Vector Machine ( SVM) technique is a hot spot.

在说话人识别领域，基于支持向量机（support Vector Machine，SVM）的识别方法是当今的研究热点。

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This thesis studies the incremental training algorithm of support vector machine ( SVM) and its application in process control.

本论文主要研究了支持向量机（support Vector Machine，简称SVM）增量型训练算法及其在控制领域中的应用。

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The problems of kernel function selection and parameter determination are analyzed.

主要研究了适于解决高维问题的支持向量机（SVM）方法在高光谱图像分类中的应用，分析了核函数选择及参数确定问题。

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An Application of Support Vector Machines in Bankruptcy Prediction Model

用支持向量机（SVM）构建企业破产预测模型

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This paper has the first times systematically studied SVM deep.

本文首次深入系统地对支持向量机（SVM）进行了研究讨论。

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Finally, we combine SVM with Incremental Study into web text mining and put forward a new SVM Incremental Study algorithm.

最后，本文将支持向量机与增量学习相结合应用到Web文本挖掘中，提出一种改进的支持向量机增量学习算法。

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Finally, we have built a Random forest classifier on the new training sets to realize the R-gene classification.

最后在重建的训练集上，利用随机森林算法构建可以识别抗性基因的分类器。

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The generalization error for forests converges to a limit as the number of trees in the forest becomes large.

当森林中决策树的数目增大，随机森林的泛化误差将趋向一个上界。

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In this paper, it does a lot of researches on random forest when used to analyze metabolomics data.

本文基于随机森林模型，针对其在代谢组学数据分析中的应用，进行了大量的研究。

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A new time series random forest algorithm combined with regular change measurement of the time series is proposed.

本文的另一内容是对代谢组学时间序列色谱数据及时间序列随机森林分类算法进行研究，给出了一种与时间序列规律性变化度量相结合的时间序列随机森林算法。

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