This HTML5 document contains 45 embedded RDF statements represented using HTML+Microdata notation.

The embedded RDF content will be recognized by any processor of HTML5 Microdata.

Namespace Prefixes

PrefixIRI
dctermshttp://purl.org/dc/terms/
dbohttp://dbpedia.org/ontology/
foafhttp://xmlns.com/foaf/0.1/
n9https://global.dbpedia.org/id/
dbthttp://dbpedia.org/resource/Template:
rdfshttp://www.w3.org/2000/01/rdf-schema#
freebasehttp://rdf.freebase.com/ns/
dbpedia-fahttp://fa.dbpedia.org/resource/
rdfhttp://www.w3.org/1999/02/22-rdf-syntax-ns#
owlhttp://www.w3.org/2002/07/owl#
wikipedia-enhttp://en.wikipedia.org/wiki/
dbphttp://dbpedia.org/property/
provhttp://www.w3.org/ns/prov#
dbchttp://dbpedia.org/resource/Category:
xsdhhttp://www.w3.org/2001/XMLSchema#
wikidatahttp://www.wikidata.org/entity/
dbrhttp://dbpedia.org/resource/

Statements

Subject Item
dbr:M-theory_(learning_framework)
dbo:wikiPageWikiLink
dbr:Sample_complexity
Subject Item
dbr:Prior-independent_mechanism
dbo:wikiPageWikiLink
dbr:Sample_complexity
Subject Item
dbr:Probably_approximately_correct_learning
dbo:wikiPageWikiLink
dbr:Sample_complexity
Subject Item
dbr:Artificial_intelligence
dbo:wikiPageWikiLink
dbr:Sample_complexity
Subject Item
dbr:Sample_complexity
rdfs:label
Sample complexity
rdfs:comment
The sample complexity of a machine learning algorithm represents the number of training-samples that it needs in order to successfully learn a target function. More precisely, the sample complexity is the number of training-samples that we need to supply to the algorithm, so that the function returned by the algorithm is within an arbitrarily small error of the best possible function, with probability arbitrarily close to 1. There are two variants of sample complexity:
dcterms:subject
dbc:Machine_learning
dbo:wikiPageID
43269516
dbo:wikiPageRevisionID
1068917677
dbo:wikiPageWikiLink
dbr:Rademacher_complexity dbr:No_free_lunch_in_search_and_optimization dbr:Vapnik–Chervonenkis_theory dbr:No_free_lunch_theorem dbr:VC_dimension dbr:Glivenko-Cantelli_class dbr:Empirical_risk_minimization dbr:Metric_learning dbr:Tikhonov_regularization dbr:Monte_Carlo_tree_search dbr:Model-free_(reinforcement_learning) dbr:Regularization_(mathematics) dbr:Random_variable dbr:Reinforcement_learning dbr:Active_learning_(machine_learning) dbc:Machine_learning dbr:Dictionary_learning dbr:Semi-supervised_learning dbr:Probably_approximately_correct_learning dbr:Online_machine_learning dbr:Machine_learning
owl:sameAs
wikidata:Q18354077 n9:mVCu dbpedia-fa:پیچیدگی_نمونه freebase:m.0114dpwp
dbp:wikiPageUsesTemplate
dbt:Machine_learning_bar dbt:Reflist
dbo:abstract
The sample complexity of a machine learning algorithm represents the number of training-samples that it needs in order to successfully learn a target function. More precisely, the sample complexity is the number of training-samples that we need to supply to the algorithm, so that the function returned by the algorithm is within an arbitrarily small error of the best possible function, with probability arbitrarily close to 1. There are two variants of sample complexity: * The weak variant fixes a particular input-output distribution; * The strong variant takes the worst-case sample complexity over all input-output distributions. The No free lunch theorem, discussed below, proves that, in general, the strong sample complexity is infinite, i.e. that there is no algorithm that can learn the globally-optimal target function using a finite number of training samples. However, if we are only interested in a particular class of target functions (e.g, only linear functions) then the sample complexity is finite, and it depends linearly on the VC dimension on the class of target functions.
prov:wasDerivedFrom
wikipedia-en:Sample_complexity?oldid=1068917677&ns=0
dbo:wikiPageLength
14205
foaf:isPrimaryTopicOf
wikipedia-en:Sample_complexity
Subject Item
dbr:Outline_of_machine_learning
dbo:wikiPageWikiLink
dbr:Sample_complexity
Subject Item
dbr:Random-sampling_mechanism
dbo:wikiPageWikiLink
dbr:Sample_complexity
Subject Item
dbr:Sample-complexity_bounds
dbo:wikiPageWikiLink
dbr:Sample_complexity
dbo:wikiPageRedirects
dbr:Sample_complexity
Subject Item
wikipedia-en:Sample_complexity
foaf:primaryTopic
dbr:Sample_complexity