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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 Backfitting algorithm ： ウィキペディア英語版 Backfitting algorithm In statistics, the backfitting algorithm is a simple iterative procedure used to fit a generalized additive model. It was introduced in 1985 by Leo Breiman and Jerome Friedman along with generalized additive models. In most cases, the backfitting algorithm is equivalent to the Gauss–Seidel method algorithm for solving a certain linear system of equations ==Algorithm== Additive models are a class of non-parametric regression models of the form: : $Y\_i\; =\; \backslash alpha\; +\; \backslash sum\_^p\; f\_j(X\_)\; +\; \backslash epsilon\_i$ where each $X\_1,\; X\_2,\; \backslash ldots,\; X\_p$ is a variable in our $p$-dimensional predictor $X$, and $Y$ is our outcome variable. $\backslash epsilon$ represents our inherent error, which is assumed to have mean zero. The $f\_j$ represent unspecified smooth functions of a single $X\_j$. Given the flexibility in the $f\_j$, we typically do not have a unique solution: $\backslash alpha$ is left unidentifiable as one can add any constants to any of the $f\_j$ and subtract this value from $\backslash alpha$. It is common to rectify this by constraining : $\backslash sum\_^N\; f\_j(X\_)\; =\; 0$ for all $j$ leaving : $\backslash alpha\; =\; 1/N\; \backslash sum\_^N\; y\_i$ necessarily. The backfitting algorithm is then: 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「Backfitting algorithm」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.