Autor:	Luc Devroye, László Györfi, Gábor Lugosi
Rok vydání:	1996
Předmět:	Discrete mathematics Empirical error Training set Vapnik–Chervonenkis theory Probability of error Decision rule Empirical measure Classifier (UML) Mathematics Abstract model theory
Zdroj:	A Probabilistic Theory of Pattern Recognition ISBN: 9781461268772
DOI:	10.1007/978-1-4612-0711-5_12
Popis:	In this chapter we select a decision rule from a class of rules with the help of training data. Working formally, let C be a class of functions O: R d → {0,1}. One wishes to select a function from C with small error probability. Assume that the training data D n = ((X 1, Y 1),..., (X n , Y n )) are given to pick one of the functions from C to be used as a classifier.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::9433c67b34c38f8a5386450616fb2d75 https://doi.org/10.1007/978-1-4612-0711-5_12 Zobrazit plný text záznamu