Definition 4.12 DF concept error boundary: The number of error categorizations of the training model to the training sample before successful convergence to a hypothesis.
4.3.2.1Dynamic fuzzy probably approximately correct (DFPAC) Learning Framework Theory
Prof. Valiant’s probably approximately correct (PAC) learning theory is very important in the development of machine learning. The PAC learning framework, first proposed in 1984, states that the evaluation of machine learning should be based on “probability approximation correctness” [3], unlike traditional pattern recognition, which is based on probabilities of 1 [4].
The basic idea of this theory is that we do not need the absolutely correct learning algorithm. We use the probability language ...
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