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dbr:Inside–outside_algorithm
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內部外部演算法 Inside–outside algorithm
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For parsing algorithms in computer science, the inside–outside algorithm is a way of re-estimating production probabilities in a probabilistic context-free grammar. It was introduced by 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). 內部外部演算法（英語：inside-outside algorithm）是一種重新檢驗隨機上下文無關文法（probabilistic context-free grammar）生成機率的方式，由James K. Baker 於1979年提出，是一個一般化的，用來作為隨機上下文無關文法其隱馬爾可夫模型的屬性評估。這種演算法是用來計算某種期望值，舉例來說，可以用來成為最大期望算法（一種無監督的學習演算法）的一部分。
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內部外部演算法（英語：inside-outside algorithm）是一種重新檢驗隨機上下文無關文法（probabilistic context-free grammar）生成機率的方式，由James K. Baker 於1979年提出，是一個一般化的，用來作為隨機上下文無關文法其隱馬爾可夫模型的屬性評估。這種演算法是用來計算某種期望值，舉例來說，可以用來成為最大期望算法（一種無監督的學習演算法）的一部分。 For parsing algorithms in computer science, the inside–outside algorithm is a way of re-estimating production probabilities in a probabilistic context-free grammar. It was introduced by 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).
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