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In computational learning theory, probably approximately correct (PAC) learning is a framework for mathematical analysis of machine learning. It was proposed in 1984 by Leslie Valiant. [1] In this framework, the learner receives samples and must select a generalization function (called the hypothesis) from a
18 mar 2024 · 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. In this tutorial, we’ll discuss the PAC learning theory and review its results. 2. Motivation
19 lip 2024 · PAC learning is a fundamental theory in machine learning that offers insights into the sample complexity and generalization of algorithms. By understanding the trade-offs between accuracy, confidence, and sample size, PAC learning helps in designing robust models.
One of the most important models of learning in this course is the PAC model. This model seeks to find algorithms which can learn concepts, given a set of labeled examples, with. hypothesis that is likely to be about right. This notion of “likely to be about right” is usually called Probably Approximately Correct (PAC).
PAC learning and consistency •Suppose we can find hypotheses that are consistent with m training instances. •We can analyze PAC learnability by determining whether 1. m grows polynomially in the relevant parameters 2. the processing time per training example is polynomial
Define the PAC model of learning. 3. Make formal connections to the principle of Occam’s razor. Can we describe or bound the true error (errD) given the empirical error (errS)? Is a concept class C learnable? Is it possible to learn C using only the functions in H using the supervised protocol?
In the eld of computational learning theory, we develop precise mathematical formulations of ‘learning from data’. Having a precise mathematical formulation allows us to answer questions such as the following: What types of functions are easy to learn? Are there classes of functions that are hard to learn? How much data is required to learn ...
