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局部线性回归方法及其稳健形式已经被看作一种有效的非参数光滑方法.与流行的核回归方法相比,它有诸多优点,诸如:较高的渐近效率和较强的适应设计能力.另外,局部线性回归能适应几乎所有的回归设计情形却不需要任何边界修正。,Local linear regression methods and their solid form has been seen as an effective non-parametric smoothing method. Contrary to popular kernel
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核函数是利用支持向量机解决不可分问题时引入的一种非线性变换的手段。基本思想是通过非线性变换，使样本变换之后的特征空间中变得线性可分。然后利用线性可分时构造最优超平面的方法，在特征空间中实现最优超平面的求解。-Kernel function is the use of support vector machine to resolve the issue can not be separated from the introduction of a nonlinear transform mean
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这个帖子中我想讨论的是移动窗口多项式最小二乘拟和平滑方法，粗糙惩罚方法，以及kernel平滑方法。-Posts in this discussion I think are moving window least squares polynomial fitting smoothing method, crude methods of punishment, as well as the kernel smoothing method.
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为解决PCA不适合多指标综合分析中非线性主成分分析的问题 ,采用核主成分分析 (kpca)方法 ,对我国不同地区 16种腐乳的品质进行了综合评价。
-PCA is not suitable to address the many indicators of a comprehensive analysis of non-linear principal component analysis of the problem, using Kernel Principal Component An
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模糊核聚类算法的几篇论文及matlab源码，可以以练代学，更好掌握模糊聚类方法。-Fuzzy Kernel Clustering Algorithm matlab several papers and source code, can be practicing on behalf of science, to better grasp the fuzzy clustering method.
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KPCA与SVM共同用于人脸识别 SVM提高了分类效果 KPCA是一种借鉴SVM中核函数的一种较好的特征提取方法-KPCA and SVM for face recognition SVM together to improve the classification results from KPCA is a kernel function in SVM a better feature extraction method
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A new approach toward target representation and localization, the central component in visual tracking of nonrigid objects,
is proposed. The feature histogram-based target representations are regularized by spatial masking with an isotropic kernel.
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Bivariate Kamma Kernel Density Estimate
for large data set-optimize method
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kernel adatron, svm impelemtation using gradient ascent method, fast and accurate for solving SVM problem with two classes
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Kernel adatron, solving svm with gradient ascend method. fast and accurate.
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Kernel Entropy Component Analysis,KECA方法的作者R. Jenssen自己写的MATLAB代码，文章发表在2010年5月的IEEE TPAMI上面-Kernel Entropy Component Analysis, by R. Jenssen, published in IEEE TPAMI 2010.
We introduce kernel entropy component analysis (kernel ECA) as a new method
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In kernel ridge regression we have seen the final solution was not sparse in the variables ® .
We will now formulate a regression method that is sparse, i.e. it has the concept of support
vectors that determine the solution.
The thing to not
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A method for classification of image using svm kernel
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核主成分分析方法，是主成分分析的一种改进算法，是一种非线性的特征提取方法。
-Kernel principal component analysis, is the principal component analysis of an improved algorithm, is a nonlinear feature extraction method.
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在ORL或Yale标准人脸数据库上完成模式识别任务。用PCA与基于核的PCA（KPCA）方法完成人脸图像的重构与识别试验.
-Or Yale in the ORL face database, complete the standard pattern recognition tasks. With the PCA and kernel-based PCA (KPCA) method to complete the reconstruction of face image and reco
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基于核函数回归方法的图像去噪，图像平滑。对于图像领域的研究者有很大作用-Kernel regression method based on image denoising, image smoothing. Researchers in the field for the image plays a significant role
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提出了基于小波核函数的偏最小二乘方法对混沌信号进行了有效拟合，得到了很好的效果。-Based on wavelet kernel function of the partial least squares method of fitting the effective chaotic signal obtained very good results.
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基于核方法的主成分分析matlab源代码，比较经典，推荐学习。-Method based on kernel principal component analysis matlab source code, more classic, recommended learning.
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利用MAT—LAB对实验数据进行了基于表面轮廓法的三维图像重构，观察不同的平滑领域参数以及轮廓提取阈值对三维重构效果的影响。-In this paper，taking advantage
of MATLAB we reconstruct experiment data into three dimension image in the way of feature-based method，
and observe the effect of different parameters，s
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用核回归方法实现图像去噪是目前理论上最先进的图像去噪方法，这里提供的是图像去噪的matlab代码。-Kernel regression method with denoising is theoretically the most advanced image denoising method, here is the matlab code for image denoising.
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