Probably Approximately Correct learning
GPTKB entity
Statements (23)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
learning theory
|
| gptkbp:abbreviation |
gptkb:PAC_learning
|
| gptkbp:assumes |
random sampling from distribution
target concept |
| gptkbp:criteria |
accuracy
confidence |
| gptkbp:describes |
feasibility of learning algorithms
|
| gptkbp:field |
computational learning theory
|
| gptkbp:focusesOn |
computational complexity
sample complexity |
| gptkbp:formedBy |
Valiant, L. G. (1984). A theory of the learnable. Communications of the ACM.
|
| gptkbp:goal |
learn a hypothesis close to target concept
|
| https://www.w3.org/2000/01/rdf-schema#label |
Probably Approximately Correct learning
|
| gptkbp:influenced |
modern machine learning theory
|
| gptkbp:introduced |
gptkb:Leslie_Valiant
|
| gptkbp:introducedIn |
1984
|
| gptkbp:relatedTo |
gptkb:VC_dimension
learning theory |
| gptkbp:studies |
learnability of concept classes
|
| gptkbp:usedIn |
theoretical computer science
supervised learning |
| gptkbp:bfsParent |
gptkb:PAC_learning
|
| gptkbp:bfsLayer |
5
|