Statements (23)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
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
|
| https://www.w3.org/2000/01/rdf-schema#label |
PAC-learning
|
| gptkbp:influenced |
development of modern machine learning theory
|
| gptkbp:introduced |
gptkb:Leslie_Valiant
|
| gptkbp:introducedIn |
1984
|
| gptkbp:mathematicalFoundation |
gptkb:probability_theory
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
|