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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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Journal ArticleDOI
Model Selection in an Information Economy: Choosing What to Learn
Chris Brooks,Robert S. Gazzale,Rajarshi Das,Jeffrey O. Kephart,Jeffrey K. MacKie-Mason,Edmund H. Durfee +5 more
TL;DR: The study of markets composed, at least in part, of autonomous agents has taken on increasing importance; an optimizing producer should consider the profit each price schedule can extract, as well as the difficulty of learning of this schedule.
Journal Article
Learning to Select Features using their Properties
TL;DR: This framework is used to analyze one universal dictionary and finds that the quality of features in this dictionary can be predicted accurately by its meta- features, referred to as meta-features.
Proceedings Article
Application of Genetic Search in Derivation of Matrix Models of Peptide Binding to MHC Molecules
Vladimir Brusic,Christian Schönbach,Masafumi Takiguchi,Victor Ciesielski,Leonard C. Harrison +4 more
TL;DR: A simulation study was performed to determine the minimum number of peptide samples required to derive matrices, given the pre-defined accuracy of the matrix model, and matrices for peptide binding to the human class I MHC molecules, HLA-B35 and -A24 were derived.
DissertationDOI
A Unified Framework for Resource-Bounded Autonomous Agents Interacting with Unknown Environments
TL;DR: A mathematical framework for conceptualizing and constructing adaptive autonomous systems under resource constraints is presented, and an information-theoretic notion of resources in autonomous systems is established.
Journal ArticleDOI
An efficient query learning algorithm for ordered binary decision diagrams
TL;DR: A new algorithm that exactly learns ordered binary decision diagrams (OBDDs) with a given variable ordering via equivalence and membership queries using at most n equivalence queries and at most 3n membership queries is proposed.