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An Introduction to Computational Learning Theory

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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.

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Journal ArticleDOI

Boosting in the presence of noise

TL;DR: This paper shows that a modified version of a boosting algorithm due to Mansour and McAllester can achieve accuracy arbitrarily close to the noise rate, and considers a variant of the standard scenario for boosting in which the ''weak learner'' satisfies a slightly stronger condition than the usual weak learning guarantee.
Proceedings ArticleDOI

Learning and verifying quantified boolean queries by example

TL;DR: In this article, the authors analyze the number of questions needed to learn or verify qhorn queries, a special class of Boolean quantified queries whose underlying form is conjunctions of quantified Horn expressions.
Proceedings ArticleDOI

Clustering for edge-cost minimization (extended abstract)

TL;DR: A randomized approximation algorithm is obtained for partitioning a set of n points into clusters, so as to minimize the sum, over all intracluster pairs of points, of the cost associated with each pair.
Proceedings ArticleDOI

Learning schema mappings

TL;DR: This work uses the lens of computational learning theory to systematically investigate the problem of obtaining algorithmically a schema mapping from data examples, and presents an efficient algorithm for learning GAV schema mappings using Angluin's model of exact learning with membership and equivalence queries.
Journal ArticleDOI

Hierarchical genetic fuzzy systems

TL;DR: It is demonstrated that the evolutionary algorithm, working together with optimization and pruning procedures, provides structurally simple fuzzy systems whose performance seems to be better than the ones produced by alternative approaches.