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PAC learning

Probably Approximately Correct (PAC) learning analyzes machine learning mathematically using probability bounds.

Papers

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Showing 211–220 of 289 papers

TitleStatusHype
Towards a combinatorial characterization of bounded memory learning—0
On Learnability with Computable Learners—0
Learning the Hypotheses Space from data: Learning Space and U-curve Property—0
On the Sample Complexity of Learning Sum-Product Networks—0
PAC learning with stable and private predictions—0
Sequential Mode Estimation with Oracle Queries—0
Learning Query Inseparable ELH Ontologies—0
On Generalization Bounds of a Family of Recurrent Neural Networks—0
Learning Concepts Definable in First-Order Logic with Counting—0
The Power of Comparisons for Actively Learning Linear Classifiers—0
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