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  • Model selection is an important research direction in SVM.

    模型选择是支持向量机一个重要的研究方向。

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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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  • Then, we implement classification modeling and forecast based on SVM.

    然后基于支持向量机进行分类建模和预测过程。

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  • This is the paper in which the relation between SVM and BPD is studied.

    文章中讨论支持向量机与基础追踪去杂讯法之间的关系。

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  • When this is complete, identify slices that are available for use by the SVM.

    完成此工作以后,标识可供SVM使用的片。

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  • The torque ripple could be significantly reduced by SVM-based DTC scheme.

    基于SVM的DTC方案可以有效地减小转矩波动。

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  • This file specifies whether the VMX (Intel) or SVM (AMD) extensions are supported.

    这个文件指定了是否支持vmx (Intel)或svm (amd)扩展。

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  • SVM for text classification - tutorial on machine learning? How do I get started?

    SVM文本分类的机器学习教程?我如何开始?

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  • The SVM saves all information about structures of volumes on a disk area called the replica.

    SVM将有关卷结构的所有信息都保存在一个称为副本(replica)的磁盘区中。

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  • However, boosting- or SVM-based methods will require more training images to get good results.

    然而,用增强型的或者基于SVM方法要得出好的结果需要更多的图片。

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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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  • In this thesis, SVM as a new machine learning method is brought into medical image classification.

    论文将支持向量机的机器学习方法引入到医学图像的分类问题。

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  • It also discusses the primary processing and recognition diagram of SVM applied to face recognition.

    还讨论支持向量机用于人脸识别的主要处理流程和识别框图。

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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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  • SVM provides you with the ability to manage large number of disks, and it also improves data availability.

    SVM为您提供管理大量磁盘的能力,同时可以改进数据的可用性。

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  • In the SVM-based speaker recognition system study, it is important to obtain a ideal recognition rate.

    在基于SVM的说话人识别系统研究中,如何获得理想的识别率是亟待解决的问题。

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  • To mirror and use LVM without VERITAS, you would have to bring these filesystems under the control of SVM.

    要在不使用VERITAS的情况下镜像和使用LVM,就必须让这些文件系统受s VM控制。

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  • To check for compatibility, run the command grep vmx /proc/cpuinfo (on AMD, run the command grep svm /proc/cpuinfo).

    为了检查兼容性,请运行命令grepvmx /proc/cpuinfo(对于 AMD,运行命令 grep svm /proc/cpuinfo)。

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  • The SVM state database stores configuration and states information about volumes, including hot spares and disk sets.

    SVM状态数据库存储有关卷的配置和状态信息,包括热备份磁盘 (hot spares) 和磁盘集。

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  • This paper presents a Support Vector Machine (SVM) method for classification of dynamic power quality disturbances.

    论文将支持向量机引入到动态电能质量分类问题中。

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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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  • This approach greatly improves the generalization ability of SVM classification and its application area is extended.

    这一方法大大提高了支持向量机分类的泛化能力,从而大大提高了支持向量机的应用范围。

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  • SVM based inverse model of nonlinear system is used as feed-forward controller to form direct inverse model controller.

    由SVM辨识的逆模型作为前馈控制器,形成直接逆模型控制器。

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  • A new prediction approach for railway passenger volume is put forward by means of Least Squares Support Vector Machine (LS-SVM).

    提出了一种基于最小二乘支持向量机(LS - SVM)的铁路客运量预测的新方法。

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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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  • This paper mainly focuses on the prediction problem by the application of hybrid and ensemble thinking into the modeling base on SVM.

    本文基于混合学习和集成学习的思想,将这两种方法应用于支持向量机建模技术中,主要解决预测分析问题。

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  • Kernel function is the key technology of SVM, the choice of kernel will affect the learning ability and generalization ability of SVM.

    核函数是SVM的关键技术,核函数的选择将影响着支持向量机的学习能力和泛化能力。

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  • Recent enhancements to SVM include support for multiterabyte volumes, cluster volume manager, and thousands of partitions per physical disk.

    最近对SVM进行的增强包括支持multiterabyte卷和ClusterVolumeManager,以及支持在每个物理磁盘上划分数千个分区。

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