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Lecture Notes in Computer Science
We extend the boosting paradigm to the realistic setting of agnostic learning, that is, to a setting where the training sample is generated by an arbitrary (unknown) probability distribution over examples and labels. We de ne a -weak agnostic learner with respect to a hypothesis class F as follows: given a distribution P it outputs some hypothesis h 2 F whose error is at most erP(F) + , where erP (F) is the minimal error of an hypothesis from F under the distribution P (note that for somedoi:10.1007/3-540-44581-1_33 fatcat:wc7x7pes6fcjlaldhpwajdtsae