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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 Averaged one-dependence estimators ： ウィキペディア英語版 Averaged one-dependence estimators Averaged one-dependence estimators (AODE) is a probabilistic classification learning technique. It was developed to address the attribute-independence problem of the popular naive Bayes classifier. It frequently develops substantially more accurate classifiers than naive Bayes at the cost of a modest increase in the amount of computation.〔Webb, G. I., J. Boughton, and Z. Wang (2005). ("Not So Naive Bayes: Aggregating One-Dependence Estimators" ). ''Machine Learning'', 58(1), 5–24. 〕 == The AODE classifier == AODE seeks to estimate the probability of each class ''y'' given a specified set of features ''x''1, ... ''x''n, P(''y'' | ''x''1, ... ''x''n). To do so it uses the formula :$\backslash hat(y\backslash mid\; x\_1,\; \backslash ldots\; x\_n)=\backslash frac(y,x\_i)\backslash prod\_^n\backslash hat(x\_j\backslash mid\; y,x\_i)\}$ \hat(y^\prime,x_i)\prod_^n\hat(x_j\mid y^\prime,x_i)} where $\backslash hat(\backslash cdot)$ denotes an estimate of $P(\backslash cdot)$, $F(\backslash cdot)$ is the frequency with which the argument appears in the sample data and ''m'' is a user specified minimum frequency with which a term must appear in order to be used in the outer summation. In recent practice ''m'' is usually set at 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「Averaged one-dependence estimators」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.