A new distribution centers site selection method is proposed, using support vector machine ( SVM), where the data derived from a fuzzy appraisal matrix is processed.

Firstly, support vector machine ( SVM) is adopted to correct 180 free energies of small or medium organic molecules based on DFT calculations.

本文首先采用支持向量机方法（SVM）对基于DFT计算结果的180个有机小尺度、中型尺度分子自由能进行了校正。

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The general IDS model based on SVM is brought forward, which comprises three parts: audit data pretreatment processor, SVM assortment machine and decision-making system.

提出了基于SVM的通用入侵检测系统模型，它主要由审计数据预处理器、支持向量机分类器和决策系统3部分组成。

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This paper applies Least Squares Support Vector Machine ( LS-SVM) to chaotic time series prediction, and compares the prediction results with BP network and RBF network.

文中将最小二乘支持向量机算法应用于混沌时间序列预测中，并同BP网络及RBF网络的预测结果进行了比较分析。

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A Sparse Tikhonov Regularized Multi-Class Support Vector Machine

稀疏Tikhonov正则化多分类支持向量机

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Least squares support vector machine ( LS-SVM) is a kind of improved support vector machine.

最小二乘支持向量机（LS-SVM）是一种改进的支持向量机。

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Objective To search an approach based on Support Vector Machine ( SVM) for detection of different abnormalities including micro-calcifications and masses from digital mammograms.

目的：利用支持向量机（SVM）对乳腺X光片图像中的病变区域进行检测和分类，识别出含钙化点区域和肿瘤区域。

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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.

根据支持向量机线性分类和可以具有不同核函数的非线性分类两种算法，建立了砂土液化预测模型，并且运用Matlab语言编写了程序。

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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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