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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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On evaluating stream learning algorithms
TL;DR: A general framework for assessing predictive stream learning algorithms and defends the use of prequential error with forgetting mechanisms to provide reliable error estimators, and proves that, in stationary data and for consistent learning algorithms, the holdout estimator, the preQUential error and the prequentially error estimated over a sliding window or using fading factors, all converge to the Bayes error.
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Ontological semantics
TL;DR: This book is divided into two parts: a philosophical part I and a practical part II, in which the authors present their text-meaning representation (TMR) and demonstrate how it is used in language analysis and critique many alternative views of semantics.
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Regularities unseen, randomness observed: levels of entropy convergence.
TL;DR: Several phenomenological approaches to applying information theoretic measures of randomness and memory to stochastic and deterministic processes are synthesized by using successive derivatives of the Shannon entropy growth curve to look at the relationships between a process's entropy convergence behavior and its underlying computational structure.
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An algorithmic theory of learning: robust concepts and random projection
Rosa I. Arriaga,Santosh Vempala +1 more
TL;DR: This work provides a novel algorithmic analysis via a model of robust concept learning (closely related to “margin classifiers”), and shows that a relatively small number of examples are sufficient to learn rich concept classes.
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Feedforward Neural Network Methodology
TL;DR: This monograph provides a through and coherent introduction to the mathematical properties of feedforward neural networks and to the computationally intensive methodology that has enabled their highly successful application to complex problems of pattern classification, forecasting, regression, and nonlinear systems modeling.