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An Introduction to Computational Learning Theory
Michael Kearns,Umesh Vazirani +1 more
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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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Feasible Iteration of Feasible Learning Functionals
TL;DR: The authors' general main results provide for strict learning hierarchies where the trial count down involves all and only notations for infinite limit ordinals, and an example of how to regain feasibility by a suitable parameterized complexity analysis.
Book ChapterDOI
Active Mining of Document Type Definitions
Markus Frohme,Bernhard Steffen +1 more
TL;DR: The point of this specification mining approach is to reveal unknown (lost or hidden) syntactic document constraints that are automatically imposed by document validators in order to support document writers or to validate whether a certain validator implementation does indeed satisfy its specification.
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Properly Learning Poisson Binomial Distributions in Almost Polynomial Time
TL;DR: In this article, the authors give an algorithm for properly learning Poisson binomial distributions of order n, where n is the discrete probability distribution of the sum of mutually independent Bernoulli random variables.
Proceedings ArticleDOI
Robust Training of a link adaptation cognitive engine
TL;DR: The novel Robust Training Algorithm (RoTA), which given at least one method that exceeds the minimum performing requirements, adaptively maintains a communication link with the minimum required performance.
Book ChapterDOI
Benchmarking Combinations of Learning and Testing Algorithms for Active Automata Learning
TL;DR: Active automata learning comprises techniques for learning automata models of black-box systems by testing such systems by using model-based analysis and verification techniques.