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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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Journal ArticleDOI
Communication Complexity Under Product and Nonproduct Distributions
TL;DR: A strong negative answer is given to the question whether the maximum is taken over product distributions only, rather than all distributions μ, and an exponential separation between the statistical query dimension and signrank is obtained.
DissertationDOI
From ordinal ranking to binary classification
TL;DR: The algorithmic and theoretical use of the reduction framework is demonstrated by extending SVM and AdaBoost, two of the most popular binary classification algorithms, to the area of ordinal ranking and it is shown that binary classification can be improved by going from a finite ensemble to an infinite one.
Book ChapterDOI
Learning Theory and Epistemology
TL;DR: Learning is the acquisition of new knowledge and skills that spans a range of processes from practice and rote memorization to the invention of entirely novel abilities and scientific theories that extend past experience.
Journal ArticleDOI
On the fusion of threshold classifiers for categorization and dimensionality reduction
TL;DR: This work studies ensembles of simple threshold classifiers for the categorization of high-dimensional data of low cardinality and gives a compression bound on their prediction risk.
Proceedings ArticleDOI
Ensemble of classifiers for detecting network intrusion
TL;DR: This paper presents an intrusion detection model based on Ensemble of classifiers such as AdaBoost, MultiBoosting and Bagging to gain more opportunity of training misclassified samples and reduce the error rate by the majority voting of involved classifiers.