A neural network expert system with confidence measurements
We show two ways to attach confidence measurements to MACIE, a connectionist expert system model. One technique involves multiple generation of neural network knowledge bases and parallel execution of expert system inference engines. Required modifications for forward and backward chaining and explanations by If-Then rules with confidence measurements are given.
In the second approach we show that inferences made by the MACIE connectionist expert system with partial information have the same confidence as inferences made with full information under certain standard probability models, including distribution-free Valiant-style learning.
Keywordsconfidence MACIE neural network expert system Valiant model
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