Description: 核主成分分析法,用matlab实现,极为精彩.不可错过-Kernel Principal Component Analysis method, using matlab realize, is extremely exciting. Not to be missed Platform: |
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Author:yyb_whu |
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Description: 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 regression methods, it has many advantages, such as: higher efficiency and stronger asymptotic adaptation design capacity. In addition, the local linear regression to adjust to the return of the design of almost all cases does not require any boundary amendment. Platform: |
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Author:wanghuaqiu |
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Description: 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 means. Basic idea is to adoption of non-linear transform, so that after changing the characteristics of the sample space become linearly separable. And the use of linear time-structure optimal hyperplane method of implementation in the feature space for solving the optimal hyperplane. Platform: |
Size: 4096 |
Author:王旭 |
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Description: 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. Platform: |
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Author:linyuan |
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Description: 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 Analysis (kpca) method, 16 kinds of different regions of our country the quality of fermented bean curd had a comprehensive evaluation. Platform: |
Size: 1024 |
Author:fengyu |
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Description: 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. Platform: |
Size: 1380352 |
Author:大长今 |
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Description: 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 Platform: |
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Author:付赛男 |
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Description: 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. The
masking induces spatially-smooth similarity functions suitable for gradient-based optimization, hence, the target localization problem
can be formulated using the basin of attraction of the local maxima. We employ a metric derived from the Bhattacharyya coefficient as
similarity measure, and use the mean shift procedure to perform the optimization. In the presented tracking examples, the new method
successfully coped with camera motion, partial occlusions, clutter, and target scale variations. Integration with motion filters and data
association techniques is also discussed. We describe only a few of the potential applications: exploitation of background information,
Kalman tracking using motion models, and face tracking. Platform: |
Size: 2459648 |
Author:Ali |
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Description: 介绍了一种非常实用的特征提取新方法，针对稀疏核主成分分析方法在特征提取中的不足, 提出了一种基于核K- 均值聚类的稀疏核主成分分析( Sparse KPCA) 的特征提取方法用于说话人识别。-Introduced a very useful new method of feature extraction for Sparse Kernel Principal Component Analysis in Feature Extraction of the lack of a kernel-based K-means clustering of sparse kernel principal component analysis (Sparse KPCA) of the feature extraction methods for speaker recognition. Platform: |
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Author:毋桂萍 |
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Description: kernel adatron, svm impelemtation using gradient ascent method, fast and accurate for solving SVM problem with two classes Platform: |
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Author:budi santosa |
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Description: 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 for data transformation and dimensionality reduction. Kernel ECA reveals structure relating to the Renyi entropy of the input space data set, estimated via a kernel matrix using Parzen windowing. This is achieved by projections onto a subset of entropy preserving kernel principal component analysis (kernel PCA) axes. This subset does not need, in general, to correspond to the top eigenvalues of the kernel matrix, in contrast to the dimensionality reduction using kernel PCA. We show that kernel ECA may produce strikingly different transformed data sets compared to kernel PCA, with a distinct angle-based structure. A new spectral clustering algorithm utilizing this structure is developed with positive results. Furthermore, kernel ECA is shown to be an useful alternative for pattern denoising. Platform: |
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Author:johhnny |
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Description: 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 notice is that the sparseness arose from complementary slackness conditions
which in turn came from the fact that we had inequality constraints. In the SVM the penalty
that was paid for being on the wrong side of the support plane was given by C
P
i » k
i for
positive integers k, where » i is the orthogonal distance away from the support plane. Note
that the term jjwjj2 was there to penalize large w and hence to regularize the solution.
Importantly, there was no penalt Platform: |
Size: 51200 |
Author:bahman |
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Description: 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. Platform: |
Size: 1024 |
Author:叶子 |
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Description: 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 recognition test. Platform: |
Size: 1024 |
Author:李海 |
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Description: 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 Platform: |
Size: 234496 |
Author:郝人 |
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Description: 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. Platform: |
Size: 417792 |
Author:武菲 |
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Search - MATLAB kernel method