Learning from Multiple Sources of Inaccurate Data

Most theoretical studies of inductive inference model a situation involving a machine M learning its environment E on following lines. M, placed in E, receives data about E, and simultaneously conjectures a sequence of hypotheses. M is said to learn E just in case the sequence of hypotheses conjectured by M stabilizes to a final hypothesis which correctly represents E.

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