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We turn now to an example of a PAC-learnable concept class. Speci cally, we consider the concept class of positive half-lines. A positive half-line is a ray extending rightwards (i.e. towards +1) from some real-valued point. All values to the right of this point are labeled positive. Values to the left are labeled negative.
19 Ιουλ 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.
18 Μαρ 2024 · In this article, we gave an explanation of the PAC learning theory and discussed some of its main results. In a nutshell, PAC learning is a theoretical framework investigating a relationship between the desired error rate and the number of samples for training a learnable classifier.
The basic idea of the PAC model is to assume that examples are being provided from a xed (but perhaps unknown) distribution over the instance space. The assumption of a xed
2 Consistency versus PAC In this section we show how one can relate learnability in the consistency model and the PAC model. Theorem 2.1 (PAC Learnability of Finite Concept Classes). Let Abe an algorithm that learns a concept class Cin the consistency model (that is, it returns h2Cwhenever a consistent concept w.r.t. Sexists).
ct (PAC) learning framework. The PAC framework helps define the class of learnable concepts in terms of the number of sample points needed to achieve an approximate solution, sample complexity, and the time and space complexity of the learning algorithm, which depends on the cost of the computational re.
We will first introduce some background definitions, then discuss empirical risk minimization, analysis of ERM, sub-Gaussian Random Variables, agnostic PAC bounds, and finally conclude with a discussion on approximation error vs. estimation error.
