A Probabilistic Theory of Pattern Recognition
Stochastic modelling and applied probabilityPublished 1 January 1996
Luc Devroye, László Györfi, Gábor Lugosi
Citations3,277
Generate an AI Snapshot to get a quick, structured summary of this paper.
Study Snapshot
ObjectiveStudy objective
MethodsResearch methodology
PopulationPopulation studied
Sample sizeSample sizes
OutcomesStudy outcomes here
ResultsStudy results comes here
LimitationsResearch study limitations comes here
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
TL;DR
The Bayes Error and Vapnik-Chervonenkis theory are applied as guide for empirical classifier selection on the basis of explicit specification and explicit enforcement of the maximum likelihood principle.
Abstract
Pattern recognition presents one of the most significant challenges for scientists and engineers, and many different approaches have been proposed. The aim of this book is to provide a self-contained
Keywords
Computer ScienceMathematics
Journal of the Franklin InstituteAn introduction to probability theory and its applications
29,966 Citations1958
Journal of the American Statistical AssociationProbability: Theory and Examples.
3,216 Citations1992Kathryn Prewitt, Richard Durrett
Journal of the American Statistical AssociationProbability Theory: Independence, Interchangeability, Martingales
1,098 Citations1998Robert Lund, Y. S. Chow +1 more