error analysis of solving neutron transport criticality problems using synthetic kernel method

research of kernel method and manifold learning for ecg recognition

核方法与流形学习在心电识别中的研究

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strong convergence of estimating error distribution based on kernel method in regression models

回归模型误差分布核估计的强收敛性

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a new gpr target feature extraction method based on kernel method and pca

基于核方法和主成分分析（PCA）的探地雷达目标特征提取新方法

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color image quantization algorithm based on kernel method

基于核方法的彩色图像量化研究

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kernel method as a non-linear dimension reduction method is widely used.

核映射是一种广泛使用的非线性映射方法。

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study of modeling based on kernel method in sparse feature space

基于稀疏特征空间的核方法建模研究

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condition monitoring technology for complicated equipment based on kernel method

基于核方法的复杂设备运行状态监测技术

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research on some problems of kernel method in pattern recognition

模式识别中核方法若干问题研究

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recently the kernel method has been used in clustering analysis.

近年来，核方法被用于聚类分析中，形成了核聚类算法。

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design of frame system for intelligent comprehensive evaluation based on kernel method

基于核方法的智能综合评价框架系统建造方法

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mutual resistance of earthing electrode

接地极互阻

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they say they seek muslim unity, driven by mutual resistance to israel and to outsiders such as the us.

他们表示他们寻求穆斯林团结，共同抵制以色列，以及抵制美国等外来者。

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in probability and statistics, the gaussian process is a common and important stochastic process, and reproducing kernel hilbert space is an important theory of computational mathematics.

在概率统计中，高斯过程是一种普遍存在和重要的随机过程，而再生核希尔伯特空间是计算数学的重要理论。

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taking advantage of the analysis of the bias, an unbiased correlator without multiplier is deduced for gaussian process and sinusoidal wave with random phase.

利用偏差分析的结果，可以得出一个对正态过程和随机相位正弦波都是无偏的不用乘法器的相关器。

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the major contributions of this thesis are following: 1. analyzed the gaussian process ( gp) for regression and the sparse gaussian process.

主要完成了以下工作：第一，分析了高斯过程以及稀疏高斯过程。

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analyze and compare the differences and relationship between the modem signal processing method based on cyclic high order cumulants and traditional signal processing method based on the gaussian process.

分析和比较了基于高阶累积量和循环平稳理论的现代信号处理方法与传统的基于高斯过程的信号处理方法的区别和联系。

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however, in practical use of gaussian process models, we find an important shortcoming of them is the high training cost rapidly increased with sample number.

研究过程中发现，高斯过程模型的训练代价会随着训练样本数的增加而快速增加。

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gaussian process latent variable model ( gplvm) is a popular manifold method recently proposed for dimensional reduction.

高斯过程隐变量模型是最近提出的比较流行的无监督降维方法。

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after transforming the stochastic component to a stationary and gaussian process, ar ( 1) model is built.

对随机项进行了平稳正态变换，并建立随机自回归AR（1）模型。

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gaussian process can regress a nonlinear function, so its performance is superior to the mmse. 3.

由于高斯过程可以对非线性的函数回归，所以基于该方法的多用户检测算法性能要优于最小均方误差。

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