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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 Inside–outside algorithm ： ウィキペディア英語版 Inside–outside algorithm In computer science, the inside–outside algorithm is a way of re-estimating production probabilities in a probabilistic context-free grammar. It was introduced James K. Baker in 1979 as a generalization of the forward–backward algorithm for parameter estimation on hidden Markov models to stochastic context-free grammars. It is used to compute expectations, for example as part of the expectation–maximization algorithm (an unsupervised learning algorithm). ==Inside and outside probabilities== The inside probability $\backslash beta\_j(p,q)$ is the total probability of generating words $w\_p\; \backslash cdots\; w\_q$, given the root nonterminal $N^j$ and a grammar $G$: :$\backslash beta\_j(p,q)\; =\; P(w\_|N^j\_,\; G)$ The outside probability $\backslash alpha\_j(p,q)$ is the total probability of beginning with the start symbol $N^1$ and generating the nonterminal $N^j\_$ and all the words outside $w\_p\; \backslash cdots\; w\_q$, given a grammar $G$:〔 :$\backslash alpha\_j(p,q)\; =\; P(w\_,\; N^j\_,\; w\_|G)$ 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「Inside–outside 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.