翻訳と辞書 Words near each other ・ Kernel ・ Kernel (algebra) ・ Kernel (category theory) ・ Kernel (digital media company) ・ Kernel (EP) ・ Kernel (image processing) ・ Kernel (linear algebra) ・ Kernel (operating system) ・ Kernel (set theory) ・ Kernel (statistics) ・ Kernel adaptive filter ・ Kernel debugger ・ Kernel density estimation ・ Kernel eigenvoice ・ Kernel embedding of distributions ・ Kernel Fisher discriminant analysis ・ Kernel function for solving integral equation of surface radiation exchanges ・ Kernel Independent Transport Layer ・ Kernel marker ・ Kernel method ・ Kernel methods for vector output ・ Kernel Normal Form ・ Kernel panic ・ Kernel patch ・ Kernel Patch Protection ・ Kernel perceptron ・ Kernel preemption ・ Kernel principal component analysis ・ Kernel random forest ・ Kernel regression Dictionary Lists mini英和辞書 mini和英辞書 Webster 1913 Latin-English FOLDOC Wikipedia English ウィキペディア

翻訳と辞書　辞書検索 [ 開発暫定版 ] スポンサード リンク Kernel Fisher discriminant analysis ： ウィキペディア英語版 Kernel Fisher discriminant analysis In statistics, kernel Fisher discriminant analysis (KFD), also known as generalized discriminant analysis and kernel discriminant analysis, is a kernelized version of linear discriminant analysis. It is named after Ronald Fisher. Using the kernel trick, LDA is implicitly performed in a new feature space, which allows non-linear mappings to be learned. ==Linear discriminant analysis== Intuitively, the idea of LDA is to find a projection where class separation is maximized. Given two sets of labeled data, $\backslash mathbf\_1$ and $\backslash mathbf\_2$, define the class means $\backslash mathbf\_1$ and $\backslash mathbf\_2$ to be : $$ \mathbf_i = \frac\sum_^\mathbf_n^i, where $l\_i$ is the number of examples of class $\backslash mathbf\_i$. The goal of linear discriminant analysis is to give a large separation of the class means while also keeping the in-class variance small. This is formulated as maximizing : $$ J(\mathbf) = \frac}\mathbf_B\mathbf}}\mathbf_W\mathbf}, where $\backslash mathbf\_B$ is the between-class covariance matrix and $\backslash mathbf\_W$ is the total within-class covariance matrix: : $$ \begin \mathbf_B & = (\mathbf_2-\mathbf_1)(\mathbf_2-\mathbf_1)^_W & = \sum_\sum_^(\mathbf_n^i-\mathbf_i)(\mathbf_n^i-\mathbf_i)^ Differentiating $J(\backslash mathbf)$ with respect to $\backslash mathbf$, setting equal to zero, and rearranging gives : $$ (\mathbf^_B\mathbf)\mathbf_W\mathbf = (\mathbf^_W\mathbf)\mathbf_B\mathbf. Since we only care about the direction of $\backslash mathbf$ and $\backslash mathbf\_B\backslash mathbf$ has the same direction as $(\backslash mathbf\_2-\backslash mathbf\_1)$ , $\backslash mathbf\_B\backslash mathbf$ can be replaced by $(\backslash mathbf\_2-\backslash mathbf\_1)$ and we can drop the scalars $(\backslash mathbf^\_B\backslash mathbf)$ and $(\backslash mathbf^\_W\backslash mathbf)$ to give : $$ \mathbf \propto \mathbf^_W(\mathbf_2-\mathbf_1). 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Kernel Fisher discriminant analysis」の詳細全文を読む スポンサード リンク 翻訳と辞書 : 翻訳のためのインターネットリソース Copyright(C) kotoba.ne.jp 1997-2016. All Rights Reserved.