Probably Approximately Correct (PAC) learning defines a mathematical relationship between the number of training samples, the error rate, and the probability that the available training data are large enough to attain the desired error rate. The PAC theory is about finding the relationship between the true error rate and the number of training samples. A classical example of Probably Approximately Correct (PAC) learning is the concept class of rectangles, where each rectangle maps a point on the plane x ∈ R2 to +1 if it's in the rectangle and −1 otherwise.
Probably Approximately Correct (PAC) learning defines a mathematical relationship between the number of training samples, the error rate, and the probability that the available training data are large enough to attain the desired error rate.
The PAC theory is about finding the relationship between the true error rate and the number of training samples.
A classical example of Probably Approximately Correct (PAC) learning is the concept class of rectangles, where each rectangle maps a point on the plane x ∈ R2 to +1 if it's in the rectangle and −1 otherwise.
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