Open AccessBook
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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Book ChapterDOI
Regular inference for state machines with parameters
TL;DR: In this paper, the authors proposed a modification of Angluin's algorithm to construct state machine models of entities of communication protocols, where the complexity grows with the size of the symbolic representation of the DFA.
Journal ArticleDOI
Learning schema mappings
TL;DR: This article 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
Applying MDL to learn best model granularity
TL;DR: Test how the theory behaves in practice on a general problem in model selection: that of learning the best model granularity, based on a provably ideal method of inference using Kolmogorov complexity.
Book ChapterDOI
Reactive Search: Machine Learning For Memory-Based Heuristics
Roberto Battiti,Mauro Brunato +1 more
TL;DR: A reactive heuristic is a technique with the ability of tuning some important parameters during execution by means of a machine learning mechanism, which raises the need of a sounder theoretical foundation of non-Markovian search techniques.
Journal ArticleDOI
Experience of Data Analytics in EDA and Test—Principles, Promises, and Challenges
TL;DR: This paper begins by introducing several key concepts in machine learning and data mining, followed by a review of different learning approaches, and describes the experience of developing a practical data mining application.