Search results
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.
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.
10 cze 2023 · PAC learning is a theoretical framework that aims to address the fundamental challenge in ML: building models that can generalize well to unseen data. The goal of PAC...
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 a 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 refers to the fact that our hypothesis is “probably” (with probability 1 ) “approximately” (up to an error of ) correct! Remark 1 There are different versions of PAC learning based on what Hand Crepresent.
The Probably Approximately Correct (PAC) learning theory, first proposed by L. Valiant (Valiant 1984), is a statistical framework for learning a task using a set of training data.
