Statistical machine learning system and methods
作者： Graham Shapiro
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摘要: A sequence walk model (4) associates connections with system states (13). The is capable of modeling systems that have line state sequences (12). Intuitively a modeled by like an object moving around set locations. the uses determine which locations will move to and moves can be used obj ect In same way in past may sates future. process from location known as mathematical properties processes been well developed over time. are parameters model.
lens.org 参考文章(18)
James Baker, Detecting repeated phrases and inference of dialogue models ,(2004)
Irad Ben-Gal, Gail Morag, Gonen Zinger, Armin Shmilovici, Stochastic modeling of time distributed sequences ,(2002)
Kentaro Toyama, Ramaswamy Hariharan, Modeling location histories ,(2004)
Jeffrey Adam Stuecheli, Pseudo random test pattern generation using Markov chains ,(2000)
David Neal Weise, Method and apparatus for improved grammar checking using a stochastic parser ,(2005)
Kristie Seymore, Andrew McCallum, Roni Rosenfeld, Learning Hidden Markov Model Structure for Information Extraction ,(1999)
David J. Holsinger, Elizabeth Charnock, Steven L. Roberts, A method and apparatus for sociological data mining ,(2003)
Akihiro Kuroda, Speech recognition method Journal of the Acoustical Society of America. ,vol. 94, pp. 3538- 3538 ,(1987) , 10.1121/1.400821
Andrei Pascovici, Automatic identification of telephone callers based on voice characteristics Journal of the Acoustical Society of America. ,vol. 125, pp. 3487- ,(2004) , 10.1121/1.3139582
Jianxiong Wu, Method and apparatus for obtaining transcriptions from multiple training utterances Journal of the Acoustical Society of America. ,vol. 108, pp. 477- ,(1997) , 10.1121/1.429554