When Does Greedy Learning of Relevant Attributes Succeed?

— A Fourier-Based Characterization —
  • Jan Arpe
  • Rüdiger Reischuk
Conference paper

DOI: 10.1007/978-3-540-73545-8_30

Part of the Lecture Notes in Computer Science book series (LNCS, volume 4598)
Cite this paper as:
Arpe J., Reischuk R. (2007) When Does Greedy Learning of Relevant Attributes Succeed?. In: Lin G. (eds) Computing and Combinatorics. COCOON 2007. Lecture Notes in Computer Science, vol 4598. Springer, Berlin, Heidelberg

Abstract

We introduce a new notion called Fourier-accessibility that allows us to precisely characterize the class of Boolean functions for which a standard greedy learning algorithm successfully learns all relevant attributes. If the target function is Fourier-accessible, then the success probability of the greedy algorithm can be made arbitrarily close to one. On the other hand, if the target function is not Fourier-accessible, then the error probability tends to one. Finally, we extend these results to the situation where the input data are corrupted by random attribute and classification noise and prove that greedy learning is quite robust against such errors.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Jan Arpe
    • 1
  • Rüdiger Reischuk
    • 1
  1. 1.Institut für Theoretische Informatik, Universität zu Lübeck, Ratzeburger Allee 160, 23538 LübeckGermany

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