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Alternative Measures of Computational Complexity with Applications to Agnostic Learning

  • Shai Ben-David
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3959)

Abstract

We address a fundamental problem of complexity theory – the inadequacy of worst-case complexity for the task of evaluating the computational resources required for real life problems. While being the best known measure and enjoying the support of a rich and elegant theory, worst-case complexity seems gives rise to over-pessimistic complexity values. Many standard task, that are being carried out routinely in machine learning applications, are NP-hard, that is, infeasible from the worst-case-complexity perspective. In this work we offer an alternative measure of complexity for approximations-optimization tasks. Our approach is to define a hierarchy on the set of inputs to a learning task, so that natural (’real data’) inputs occupy only bounded levels of this hierarchy and that there are algorithms that handle in polynomial time each such bounded level.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Shai Ben-David
    • 1
  1. 1.School of Computer ScienceUniversity of WaterlooWaterloo, OntarioCanada

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