Contents
What is the C in the PAC model?
Generalizing the case of conjunctions, we can relate the Consistency and the PAC model as follows. examples to output a hypothesis of error at most ϵ with probability at least 1−δ. Therefore, A is a PAC-learning algorithm for learning C (by C) in the PAC model so long as this quantity is polynomial in size(c) and n.
What is PAC Learning explain with example?
Probably approximately correct (PAC) learning is a theoretical framework for analyzing the generalization error of a learning algorithm in terms of its error on a training set and some measure of complexity. The goal is typically to show that an algorithm achieves low generalization error with high probability.
What is meant by probably approximately correct learning?
How is PAC learnability different from statistical learning?
We also emphasize that the definition of PAC learnability that we have presented here seeks efficiency in terms of both time complexity as well as sample complexity. This is in contrast to statistical learning theory, where the focus is typically only on sample complexity.
Which is the correct model for PAC learning?
We begin by discussing (some variants of) the PAC (Probably Approximately Correct) learning model introduced by Leslie Valiant. Throughout this section, we will deal with a hypothesis class or concept class, denoted by ; this is a space of functions , where for some and is some finite set.
Can a PAC learner be an improper concept?
A slight weakening of the above notion is that of an improper PAC learner, under which the returned by the algorithm need not belong to the concept class (but all of the other properties of a PAC learner must still hold). Before proceeding further, we make a few remarks on the assumptions underlying the PAC learning model.
Can a PAC learner perform well under a bad distribution?
On the other hand, the requirement that a PAC learner should perform well under any distribution on the domain space can be quite restrictive, and indeed, it is the case that proofs of theorems about non-PAC-learnability sometimes involve carefully constructing a “bad” distribution for a given algorithm.