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|Title:||Probabilistic Inference: Theory and Practice (Learning, Inductive, Logic, Synthesis)|
|Author(s):||Lee, Won Don|
|Department / Program:||Computer Science|
|Degree Granting Institution:||University of Illinois at Urbana-Champaign|
|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.
Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1986.
|Date Available in IDEALS:||2014-12-15|