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6下载：
统计模式识别工具箱（Statistical Pattern Recognition Toolbox）包含：
1，Analysis of linear discriminant function
2，Feature extraction: Linear Discriminant Analysis
3，Probability distribution estimation and clustering
4，Support Vector and other Kernel Machines,
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核主成分分析算法KPCA 的matlab程序/代码
基于二维数据的。-kernel principal component analysis
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kpca 基于核主成分分析的源程序，有注释，希望对大家有帮助！-kpca based on kernel principal component analysis, source code, there are comments, want to help you!
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一个很好的核主成分分析matlab程序应用举例。该程序是在前人的核主成分分析程序基础上做了适当的修改产生的，可用于多维数据的降维和压缩处理。-A good kernel principal component analysis matlab application procedures, for example. The program is in the predecessors of Kernel Principal Component Analysis based on the proce
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为了准确地对监控场景中的运动目标进行语义上的分类，提出了一种基于聚类的核主成分分析梯度方向直方图和二又决策树支持向量机的运动目标分类算法。-In order to accurately monitor the movement of scene targets semantic classification, the clustering based on kernel principal component analysis of gradient direction histograms,
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降维工具箱，包含主元分析（PCA）,核主元分析（KPCA）等。-Dimensionality reduction kit, including principal component analysis (PCA), Kernel Principal Component Analysis (KPCA) and so on.
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Kernel principal component analysis (kernel PCA) [1] is an extension of principal component analysis (PCA) using techniques of kernel methods. Using a kernel, the originally linear operations of PCA are done in a reproducing kernel Hilbert space with
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Kernel Principal Component Analysis
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核主成分分析中使用多项式核函数时的MATLAB代码，有注释，易看懂。-Kernel Principal Component Analysis in the use of polynomial kernel function of the MATLAB code, annotated, easy read.
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kpca核主成分分析用于故障诊断与辨识中，具有很强的应用价值-kpca kernel principal component analysis for fault diagnosis and identification, has a strong value
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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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svm核主成分分析，简单实用，毕业论文程序-svm kernel principal component analysis, simple and practical, graduation procedures
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subset kernel Principal component analysis
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Kernel principal component analysis (kernel PCA) is an extension of principal component analysis (PCA) using techniques of kernel methods. Using a kernel, the originally linear operations of PCA are done in a reproducing kernel Hilbert space with a n
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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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基于聚类分析的核主成分分析，简单实用，希望对大家有帮助。-Based on cluster analysis of kernel principal component analysis, simple and practical, we want to help.
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将核思想运用到主成分分析中，使其映射到高维空间，而计算主成分的目的是将高维数据投影到较低维空间。-Kernel Principal Component Analysis
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自己尝试编写的核主元分析（KPCA）程序-Try to write a kernel principal component analysis ( KPCA ) program
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核主元分析模型，用于故障检测，输入建模数据和待检测数据，计算T2和SPE统计量-Kernel Principal Component Analysis model for fault detection, input data for modeling and data to be detected, calculated T2 and SPE statistics
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核函数的实现，核分析 和核主成份分析，核Fisher判别 的matlab实现-Nuclear function, nuclear analysis and kernel principal component analysis, kernel Fisher matlab implementation
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