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  • Support vector machine (SVM) is a new learning machine based on the statistical learning theory.

    支持向量机是一种基于统计学习理论的新型机器学习方法。

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  • Support vector machine constructs an optimal hyperplane utilizing a small set of vectors near boundary.

    支持向量机利用接近边界的少数向量来构造一个最优分类面。

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  • Support vector machine (SVM) is a new generation machine learning technique based on the statistical learning theory.

    支持向量机(SVM)是基于统计学习理论的新一代机器学习技术。

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  • The support vector machine is a learning algorithm, which has a good classification ability for limited training samples.

    支撑矢量机是一种能在训练样本数很少的情况下达到很好分类推广能力的学习算法。

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  • Support vector machine is a new kind of machine learning method.

    支持向量机是一种新的机器学习方法。

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  • Support vector machine (SVM) is a new machine learning technique.

    支持向量机(SVM)是一种新型的机器学习方法。

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  • Automatic video caption location based on support vector machine.

    基于支持向量机的视频字幕自动定位方法。

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  • The applications of support vector machine are reviewed in DNA microarray.

    就支持向量机在DNA微阵列的应用做一综述。

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  • In this paper, we presented support vector machine and applications in concept design.

    本文研究了支持向量机及其在概念设计中的应用。

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  • A laser welding quality monitoring method is proposed, based on support vector machine.

    提出了一种基于支持向量机的激光焊接质量监测方法。

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  • Validating the effectiveness of support vector machine for handwritten digit recognition.

    验证支持向量机用于手写数字识别的有效性。

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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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  • Application of theory of support vector machine for forecasting link travel time is discussed.

    主要探讨支持向量机理论在路段行程时间预测中的应用。

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  • A network traffic anomaly detection mechanism is presented based on support vector machine (SVM).

    提出了一种基于支持向量机的网络流量异常检测方法。

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  • This paper presents a recognition method of similar Chinese handwriting by support vector machine.

    本文提出对手写相似汉字进行识别的支持向量机方法。

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  • The support vector machine(SVM) is a new learning technique based on the statistical learning theory.

    支持向量机(SVM)是根据统计理论提出的一种新的学习算法。

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  • A new method of infrared spectrum analysis based on support vector machine for mixture gas was proposed.

    介绍了一种基于支持向量机的混合气体红外光谱组分浓度和种类分析的新方法。

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  • We also discussed the application of support vector machine in multivariate classification with some examples.

    还对支持向量机在多元分类中的应用进行了讨论,并给出了实例。

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  • Presents an efficient method of fabric defect classification based on cluster analysis and support vector machine (SVM).

    提出一种基于聚类分析和支持向量机(SVM)的布匹瑕疵分类方法。

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  • Transductive inference based on support vector machine is a relatively new research region in statistical learning theory.

    基于支持向量机的直推式学习是统计学习理论中一个较新的研究领域。

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  • The support vector machine (SVM) is an algorithm based on structure risk minimizing principle and high generalization ability.

    支持向量机(SVM)是一种基于结构风险最小化原理,具有很好推广性能的学习算法。

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  • This article presents a new method of plant classification of iris based on the support vector machine classification principle.

    提出了基于支持向量机的分类原理对鸢尾属植物进行分类的方法。

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  • A novel prediction model for remaining capacity of batteries based on least square support vector machine (LS-SVM) was proposed.

    提出了一种最小二乘支持向量机的电池剩余电量预测新模型。

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  • We presented a novel method for forecasting landslide displacement based on phase space reconstruction and support vector machine.

    提出了一种基于相空间重构与支持向量机预测滑坡位移的新方法。

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  • Compared with the single suppo vector machine method, the support vector machine ensemble method has better classification accuracy.

    模拟实验结果表明,该方法具有明显优于单一支持向量机的更高的分类准确率。

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  • The results indicate that the support vector machine possesses extensive application prospects in processing earthquake precursory data.

    研究结果表明,支持向量机方法在地震前兆数据处理中有着广泛的应用前景。

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  • Applied support vector machine to foundation engineering, and proposed support vector machine method to compute the foundation bearing capacity.

    将支持向量机方法应用到地基工程中,提出了计算地基承载力的支持向量机方法。

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  • Our tests show that the veracity and stability of support vector machine are both obviously superior to the traditional linear classifying methods.

    多组试验表明,支持向量机方法的准确性和稳定性较传统线性分类方法均具有明显优势。

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  • The nonlinear support vector machine multiuser detector can approach the optimum multiuser detector in a better way than the other linear detectors.

    另外,支撑矢量机多用户检测器的非线性特性可以比线性检测器更好地逼近最优检测器。

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