• The Support Vector Machine (SVM) can flexible to decide boundary in a high-dimensional feature space, because of its strong global convergence.

    支持向量(SVM)能够一个高维特征空间中灵活判别边界具有很强全局收敛性

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  • Feature space is high dimensional and sparse in text categorization, the process of dimension reduction is a very key problem for large-scale text categorization.

    文本分类特征向量空间维和稀疏,降处理是分类的关键步骤。

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  • The received mixing signals are first mapped to high-dimensional kernel feature space, and a feature vector basis given by the fitness function of the kernel feature space is constructed.

    接收混合信号首先被映射高维内核特征空间内核特征空间上的适应度函数给出的特征矢量基础构造。

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  • In this approach, the integral operator kernel functions is used to realize the nonlinear map from the raw feature space of gear vibration signals to high dimensional feature space.

    方法通过计算齿轮振动信号原始特征空间的内函数实现原始特征空间高维特征空间非线性映射

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  • In this approach, the integral operator kernel functions is used to realize the nonlinear map from the raw feature space of gear vibration signals to high dimensional feature space.

    方法通过计算齿轮振动信号原始特征空间的内函数实现原始特征空间高维特征空间非线性映射

    youdao

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