CS485 Lecture Notes - Lecture 5: Independent And Identically Distributed Random Variables, Uniform Convergence, Vaccinia

• School: University of Waterloo
• Department: Computer Science
• Course: CS485
• Professor: Shai Ben- David

Document Summary

H st and a loss function . for every spr m[ ( a "s ) ) > mein ( ( h ) )te ] f. Under some setup , h has the uniform convergence property if for some mhh : ( 0,112 . I ( h ) - ( h ) i > e) < s once. H has the uniform convergence property , agnostic. Ermh m ( e uniform convergence algorithm agnostically - pac function for. The uniform convergence property ( and , therefore , is agnostic. X~d ( x , ( h ) for where. Given proof : every prob l( h , ) every misestimates sample distribution hypothesis. ( h ) = e 3l( h h every. Iron [ / ( h ) tn # oi hypothesis. Theorem , we will now employ the union bound . Pr [ ah eh :/ ( h ) ( h ) / > e)