Presentation 1997/11/13
A Symbolic-Statistical Modeling Language PRISM
Yoshitaka KAMEYA, Taisuke SATO,
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Abstract(in English) Many statistical models are used in many application fields for an analysis of data which are uncertain, incomplete, or derived from highly complicated phenomena. This paper presets a symbolic-statistical modeling language PRISM and its programming system, which are theoretically based on distributional semantics and a learning algorithm for BS program. PRISM programming system is designed to be a tool for the integration of probability theory and logical reasoning by logic programs. Furthermore, it is equipped with a built-in learning mechanism for the parameters buried in a programs. Furthermore, it is equipped with a built-in learning mechanism for the parameters buried in a program. We explain a detail of PRISM programs which is an extension of BS programs, and their learning algorithm. Then an overview of the programming system is presented. Lastly, we take up a person's blood type genes by using his model.
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Keyword(in English) statistical models / logic programs / distributional semantics / the EM algorithm
Paper # AI97-33
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Committee AI
Conference Date 1997/11/13(1days)
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Registration To Artificial Intelligence and Knowledge-Based Processing (AI)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) A Symbolic-Statistical Modeling Language PRISM
Sub Title (in English)
Keyword(1) statistical models
Keyword(2) logic programs
Keyword(3) distributional semantics
Keyword(4) the EM algorithm
1st Author's Name Yoshitaka KAMEYA
1st Author's Affiliation Dept. of Computer Science, Tokyo Institute of Technology()
2nd Author's Name Taisuke SATO
2nd Author's Affiliation Dept. of Computer Science, Tokyo Institute of Technology
Date 1997/11/13
Paper # AI97-33
Volume (vol) vol.97
Number (no) 373
Page pp.pp.-
#Pages 8
Date of Issue