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gptkbp:instanceOf
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gptkb:learning_theory
|
|
gptkbp:assumes
|
examples are drawn independently from a fixed but unknown distribution
|
|
gptkbp:describes
|
framework for studying learnability of functions
|
|
gptkbp:field
|
gptkb:machine_learning
computational learning theory
|
|
gptkbp:fullName
|
gptkb:Probably_Approximately_Correct_learning
|
|
gptkbp:goal
|
find a hypothesis that is probably approximately correct
|
|
gptkbp:hasConcept
|
algorithm can learn a function with high probability and small error
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|
gptkbp:influenced
|
development of modern machine learning theory
|
|
gptkbp:introduced
|
gptkb:Leslie_Valiant
|
|
gptkbp:introducedIn
|
1984
|
|
gptkbp:mathematicalFoundation
|
gptkb:probability_theory
gptkb:learning_theory
|
|
gptkbp:parameter
|
delta (confidence)
epsilon (accuracy)
|
|
gptkbp:relatedConcept
|
gptkb:empirical_risk_minimization
gptkb:VC_dimension
sample complexity
agnostic learning
|
|
gptkbp:usedIn
|
theoretical analysis of machine learning algorithms
|
|
gptkbp:bfsParent
|
gptkb:David_McAllester
|
|
gptkbp:bfsLayer
|
7
|
|
https://www.w3.org/2000/01/rdf-schema#label
|
PAC-learning
|