In computer science, computational learning theory (or just learning theory) is a subfield of artificial intelligence devoted to studying the design and...

three major branches: automata theory and formal languages, computability theory, and computational complexity theory, which are linked by the question:...

Algorithmic learning theory, a branch of computational learning theory. Sometimes also referred to as algorithmic inductive inference. Computational learning theory...

theory, cybernetics, quantitative psychology, machine learning, artificial neural networks, artificial intelligence and computational learning theory;...

algorithmic game theory, machine learning, computational biology, computational economics, computational geometry, and computational number theory and algebra...

Statistical learning theory is a framework for machine learning drawing from the fields of statistics and functional analysis. Statistical learning theory deals...

time-varying framework, which impacts both the computation and efficiency of the system. To address the computational challenges introduced by this time-variance...

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

performance bounds, learning theorists study the time complexity and feasibility of learning. In computational learning theory, a computation is considered...

Testing Qualifications Board. In computational learning theory, learnability is the mathematical analysis of machine learning. It is also employed in language...

statistics Computational learning theory – studying the design and analysis of machine learning algorithms. Grammar induction Meta-learning Adversarial...

network in 1954 at MIT. They used computational machines, then called "calculators". Other neural network computational machines were created by Rochester...

Vapnik–Chervonenkis theory (also known as VC theory) was developed during 1960–1990 by Vladimir Vapnik and Alexey Chervonenkis. The theory is a form of computational learning...

The distributional learning theory or learning of probability distribution is a framework in computational learning theory. It has been proposed from...

and statistical learning theory are concerned with machine learning and can thus be viewed as branches of computational learning theory[citation needed]...

reinforcement learning is studied in many disciplines, such as game theory, control theory, operations research, information theory, simulation-based...

R. (1995). "Predictive Hebbian learning". Proceedings of the eighth annual conference on Computational learning theory - COLT '95. pp. 15–18. doi:10.1145/225298...

and Machine Learning Toolbox. In recent years, due to growing computational power, which allows for training in large ensemble learning in a reasonable...

processes Computational learning theory Inductive bias Overfitting (machine learning) (Uncalibrated) class membership probabilities Unsupervised learning Version...

n}-valued functions". Proceedings of the fifth annual workshop on Computational learning theory – COLT '92. p. 333. doi:10.1145/130385.130423. ISBN 089791497X...

accepted definition of computational intelligence. Generally, computational intelligence is a set of nature-inspired computational methodologies and approaches...

researcher in computational learning theory and algorithmic game theory, and interested in machine learning, artificial intelligence, computational finance...

connection between any units. However, learning is impractical using general Boltzmann Machines because the computational time is exponential to the size of...

margin classifiers". Proceedings of the fifth annual workshop on Computational learning theory – COLT '92. p. 144. CiteSeerX 10.1.1.21.3818. doi:10.1145/130385...

Annual Meeting on Association for Computational Linguistics - ACL '01. Morristown, NJ, USA: Association for Computational Linguistics: 26–33. doi:10.3115/1073012...

learning algorithms for recurrent networks and their computational complexity". In Chauvin, Yves; Rumelhart, David E. (eds.). Backpropagation: Theory...

Computational economics is an interdisciplinary research discipline that combines methods in computational science and economics to solve complex economic...

in computational learning theory of how a machine learning algorithm output is changed with small perturbations to its inputs. A stable learning algorithm...

In computational learning theory, Occam learning is a model of algorithmic learning where the objective of the learner is to output a succinct representation...

Annual Meeting of the Association for Computational Linguistics. Cambridge, MA: Association for Computational Linguistics: 189–196. doi:10.3115/981658...