computational learning theory, probably approximately correct (PAC) learning is a framework for mathematical analysis of machine learning. It was proposed...

viewpoint, probably approximately correct (PAC) learning provides a framework for describing machine learning. The term machine learning was coined in...

approaches include: Exact learning, proposed by Dana Angluin[citation needed]; Probably approximately correct learning (PAC learning), proposed by Leslie Valiant;...

intractable. He created the Probably Approximately Correct or PAC model of learning that introduced the field of Computational Learning Theory and became a theoretical...

algorithms that are provable boosting algorithms in the probably approximately correct learning formulation can accurately be called boosting algorithms...

subspace learning Naive Bayes classifier Maximum entropy classifier Conditional random field Nearest neighbor algorithm Probably approximately correct learning...

in polynomial time. An example of such a framework is probably approximately correct learning [citation needed]. The concept was introduced in E. Mark...

Gold. Subsequently known as Algorithmic learning theory. Probably approximately correct learning (PAC learning) proposed in 1984 by Leslie Valiant Gold...

generative models also began in the 1970s. A probably approximately correct learning bound for semi-supervised learning of a Gaussian mixture was demonstrated...

modeling Probably approximately correct learning (PAC) learning Ripple down rules, a knowledge acquisition methodology Symbolic machine learning algorithms...

knowledge (i.e., class) representability: Rough sets Probably approximately correct learning (PAC learning) Bold hypothesis Groner, Rudolf & Groner, Marina...

polynomial-time quantum algorithms which are correct WHP. Probably approximately correct learning: A process for machine-learning in which the learned function has...

Kernel method Statistical learning theory Rademacher complexity Vapnik–Chervonenkis dimension Probably approximately correct learning Probability distribution...

introduced Probably Approximately Correct Learning (PAC Learning), a framework for the mathematical analysis of machine learning. Symbolic machine learning encompassed...

properties, have important applications in machine learning, in the area of probably approximately correct learning. In computational geometry, they have been...

received training data. This is closely related to probably approximately correct (PAC) learning, where the learner is evaluated on its predictive power...

. Machine learning Data mining Probably approximately correct learning Adversarial machine learning Valiant, L. G. (August 1985). Learning Disjunction...

implementation of the online Q-learning algorithm, with probably approximately correct (PAC) learning. Greedy GQ is a variant of Q-learning to use in combination...

of steps). A weaker formal model of learnability is the Probably approximately correct learning (PAC) model, introduced by Leslie Valiant in 1984. It is...

and they begin to babble later on in infancy—at approximately 11 months as compared to approximately 6 months for hearing babies. Prelinguistic language...

probably approximately correct (PAC) model was applied by D. Roth (2002) to solve computer vision problem by developing a distribution-free learning theory...

(XAI), often overlapping with interpretable AI, or explainable machine learning (XML), either refers to an artificial intelligence (AI) system over which...

written without having to write as much code", and that "it is not always correct, but it is just close enough". According to a paper written by OpenAI researchers...

In the theory of Probably Approximately Correct Machine Learning, the Natarajan dimension characterizes the complexity of learning a set of functions...

assumptions). A natural model of passive learning is Valiant's probably approximately correct (PAC) learning. Here the learner receives random examples...

language generation. As language models, LLMs acquire these abilities by learning statistical relationships from vast amounts of text during a self-supervised...

successfully maps words onto the correct objects, concepts, and actions. While domain-specific accounts of word learning argue for innate constraints that...

Cartridge, an integrated circuit packaging type Probably approximately correct, in machine learning Presentation–abstraction–control, in software architecture...

Dialogic learning is learning that takes place through dialogue. It is typically the result of egalitarian dialogue; in other words, the consequence of...

languages, the correct use of prepositions in the English language is difficult to learn, and it can turn out to be quite a frustrating learning experience...