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翻訳と辞書　辞書検索 [ 開発暫定版 ] スポンサード リンク Relevance vector machine ： ウィキペディア英語版 Relevance vector machine In mathematics, a relevance vector machine (RVM) is a machine learning technique that uses Bayesian inference to obtain parsimonious solutions for regression and probabilistic classification. The RVM has an identical functional form to the support vector machine, but provides probabilistic classification. It is actually equivalent to a Gaussian process model with covariance function: :$k(\backslash mathbf,\backslash mathbf)\; =\; \backslash sum\_^N\; \backslash frac\; \backslash varphi(\backslash mathbf,\backslash mathbf\_j)\backslash varphi(\backslash mathbf\text{'},\backslash mathbf\_j)$ where $\backslash varphi$ is the kernel function (usually Gaussian),$\backslash alpha\_j$'s as the variances of the prior on the weight vector $w\; \backslash sim\; N(0,\backslash alpha^I)$ ,and $\backslash mathbf\_1,\backslash ldots,\backslash mathbf\_N$ are the input vectors of the training set. Compared to that of support vector machines (SVM), the Bayesian formulation of the RVM avoids the set of free parameters of the SVM (that usually require cross-validation-based post-optimizations). However RVMs use an expectation maximization (EM)-like learning method and are therefore at risk of local minima. This is unlike the standard sequential minimal optimization (SMO)-based algorithms employed by SVMs, which are guaranteed to find a global optimum (of the convex problem). The relevance vector machine is patented in the United States by Microsoft. == See also == * Kernel trick * Platt scaling: turns an SVM into a probability model 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「Relevance vector machine」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.