內部外部演算法

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生成機率的方式，由詹姆斯·K·貝克於1979年提出，是一個一般化的向前向後演算法，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。

參考資料
 J. Baker (1979): Trainable grammars for speech recognition. In J. J. Wolf and D. H. Klatt, editors, Speech communication papers presented at the 97th meeting of the Acoustical Society of America, pages 547–550, Cambridge, MA, June 1979. MIT.
 Karim Lari, Steve J. Young (1990): The estimation of stochastic context-free grammars using the inside-outside algorithm. Computer Speech and Language, 4:35–56.
 Karim Lari, Steve J. Young (1991): Applications of stochastic context-free grammars using the Inside-Outside algorithm. Computer Speech and Language, 5:237-257.
 Fernando Pereira, Yves Schabes (1992): Inside-outside reestimation from partially bracketed corpora. Proceedings of the 30th annual meeting on Association for Computational Linguistics, Association for Computational Linguistics, 128-135.
 Christopher D. Manning, Heinrich Shütze (1999): Foundations of statistical natural language processing.

外部連結
 An implementation of the algorithm by Mark Johnson



Category:分析演算法