Probabilistic inference: theory and practice
W D Lee · 1986
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.