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An Introduction to Computational Learning Theory
1994Emphasizing issues of computational efficiency, Michael Kearns and Umesh Vazirani introduce a number of central topics in computational learning theory for researchers and students in artificial intelligence, neural networks, theoretical computer science, and statistics.
Michael J. Kearns, Umesh V. Vazirani
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Computational Learning Theories
Advances in Analytics for Learning and TeachingDirk Ifenthaler, David C Gibson
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Remarks on computational learning theory
Annals of Mathematics and Artificial Intelligence, 2000zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Theories of learning: a computer game perspective
Fifth International Symposium on Multimedia Software Engineering, 2003. Proceedings., 2003Computer games provide a good environment for learning. Players learn to play the game without being taught didactically as the learning process takes place naturally in the virtual world. Learning is no longer a process of knowledge transfer from the expert to the novice.
Siang, Ang Chee +1 more
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2021
As the name suggests, computational learning theory is about ‘‘learning”’ by ‘‘computation” and is the theoretical foundation of machine learning. It aims to analyze the difficulties of learning problems, provides theoretical guarantees for learning algorithms, and guides the algorithm design based on theoretical analysis.
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As the name suggests, computational learning theory is about ‘‘learning”’ by ‘‘computation” and is the theoretical foundation of machine learning. It aims to analyze the difficulties of learning problems, provides theoretical guarantees for learning algorithms, and guides the algorithm design based on theoretical analysis.
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Proceedings of the twenty-fourth annual ACM symposium on Theory of computing - STOC '92, 1992
Permission to copy without fee all or part of this material ie granted provided that the copies are not made or distributed for direct commercial advantage, the ACM copyright notice and the title of the publication and its date appear, and notica is given that copying is by permission of the Association for Computing Machinery. To copy otherwise, or to
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Permission to copy without fee all or part of this material ie granted provided that the copies are not made or distributed for direct commercial advantage, the ACM copyright notice and the title of the publication and its date appear, and notica is given that copying is by permission of the Association for Computing Machinery. To copy otherwise, or to
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2015
The conditions for learnability of a task by a computer algorithm provide guidance for understanding their performance and guidance for selecting the appropriate learning algorithm for a particular task. In this chapter, we present the two main theoretical frameworks—probably approximately correct (PAC) and Vapnik–Chervonenkis (VC) dimension—which ...
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The conditions for learnability of a task by a computer algorithm provide guidance for understanding their performance and guidance for selecting the appropriate learning algorithm for a particular task. In this chapter, we present the two main theoretical frameworks—probably approximately correct (PAC) and Vapnik–Chervonenkis (VC) dimension—which ...
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Theorem Provers as a Learning Tool in Theory of Computation
Proceedings of the 2017 ACM Conference on International Computing Education Research, 2017This paper presents first results of an evaluation study investigating whether an interactive theorem prover like Coq can be used to help undergraduate computer science (CS) students learn mathematical proving within the field of theory of computation. Set within an educational design research approach and building on cognitive apprenticeship and socio
Maria Knobelsdorf +3 more
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