Models for sand liquefaction prediction were established according to the linear classify algorithm and the nonlinear classify algorithm of SVM, and a program was compiled in language Matlab.

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

提出了一种联合使用硬C均值（Hard C-mean，HCM）聚类算法和支持向量机（support Vector Machine，SVM）的电力系统短期负荷预测方法。

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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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