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How is the PAC framework used in machine learning?
The model was later extended to treat noise (misclassified samples). An important innovation of the PAC framework is the introduction of computational complexity theory concepts to machine learning.
Why do we use OLS in linear regression?
The same reasoning holds for our α: Once obtained those values of α and β which minimize the squared errors, our model’s equation will look like that: To sum up, you can consider the OLS as a strategy to obtain, from your model, a ‘straight line’ which is as close as possible to your data points.
Which is an example of a PAC learnable problem?
In order to give the definition for something that is PAC-learnable, we first have to introduce some terminology. For the following definitions, two examples will be used. The first is the problem of character recognition given an array of bits encoding a binary-valued image.
How is OLS used to minimize squared errors?
To be more precise, the model will minimize the squared errors: indeed, we do not want our positive errors to be compensated by the negative ones, since they are equally penalizing for our model. This procedure is called Ordinary Least Squared error — OLS.
Which is the correct way to evaluate PAC learning?
This method of evaluating learning is called Probably Approximately Correct (PAC)Learning and will be defined more precisely in the next section. Our problem, for a given concept to be learned, and given epsilon and delta, is to determine the size of the training set.
Is there a lower bound for PAC learning?
Surprisingly, we can derive a general lower bound on the size m of the training set required in PAC learning. We assume that for each x in the training set we will have h(x) = f(x), that is, the hypothesis is consistent with the target concept on the training set.
How are Epsilon and Delta used in PAC learning?
The smaller epsilon and delta are, the better the leaned concept will be. This method of evaluating learning is called Probably Approximately Correct (PAC)Learning and will be defined more precisely in the next section. Our problem, for a given concept to be learned, and given epsilon and delta, is to determine the size of the training set.