Probabilistic Inference: Theory and Practice (Learning, Inductive, Logic, Synthesis)
Lee, Won Don
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https://hdl.handle.net/2142/69551
Description
Title
Probabilistic Inference: Theory and Practice (Learning, Inductive, Logic, Synthesis)
Author(s)
Lee, Won Don
Issue Date
1986
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Computer Science
Abstract
This thesis presents a system and a methodology for probabilistic learning from examples.
First, it describes a new methodology, Probabilistic Rule Generator (PRG), of variable-valued logic synthesis which can be applied effectively to noisy data. Then, an application of the methodology to the sleep stage scoring problem is presented. A method of the communication between a human expert and a machine is described next. Finally, a new system, Probabilistic Inference, which can generate concepts with limited time and/or resources is defined. It is described how PRG can be a practical tool for Probabilistic Inference.
A departure from the classical viewpoint in logic minimization, in rule-refinement, and in knowledge acquisition is reported.
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