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PAC learning

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

Papers

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Showing 281–289 of 289 papers

TitleStatusHype
Optimal Bounds on Approximation of Submodular and XOS Functions by Juntas—0
Representation, Approximation and Learning of Submodular Functions Using Low-rank Decision Trees—0
Learning Halfspaces with the Zero-One Loss: Time-Accuracy Tradeoffs—0
Learning pseudo-Boolean k-DNF and Submodular Functions—0
Learning DNF Expressions from Fourier Spectrum—0
A Unified Framework for Approximating and Clustering Data—0
A Complete Characterization of Statistical Query Learning with Applications to Evolvability—0
Introduction to Machine Learning: Class Notes 67577Code0
PAC learning with nasty noise—0
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