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An Introduction to Computational Learning Theory
Michael Kearns,Umesh Vazirani +1 more
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TLDR
The probably approximately correct learning model Occam's razor the Vapnik-Chervonenkis dimension weak and strong learning learning in the presence of noise inherent unpredictability reducibility in PAC learning learning finite automata is described.Abstract:
The probably approximately correct learning model Occam's razor the Vapnik-Chervonenkis dimension weak and strong learning learning in the presence of noise inherent unpredictability reducibility in PAC learning learning finite automata by experimentation appendix - some tools for probabilistic analysis.read more
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Traffic Sign Detection
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Query-Efficient Algorithms for Polynomial Interpolation over Composites
TL;DR: The interpolation algorithm is used to design algorithms for zero-testing and distributional learning of polynomials over Zm .
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Data Complexity Issues in Grammatical Inference
TL;DR: It is argued that there are three levels at which data complexity for grammatical inference can be studied, and that the main difficulties arise from the fact that the structural definitions of the languages and the topological measures do not match.
Posted Content
Bounding the Fat Shattering Dimension of a Composition Function Class Built Using a Continuous Logic Connective
TL;DR: Sauer's Lemma is explained, which involves the VC dimension and is used to prove the equivalence of a concept class being distribution-free PAC learnable and it having finite VC dimension, and the construction of a new function class from a collection of function classes.