Projective DNF Formulae and Their Revision

  • Robert H. Sloan
  • Balázs Szörényi
  • György Turán
Conference paper

DOI: 10.1007/978-3-540-45167-9_45

Part of the Lecture Notes in Computer Science book series (LNCS, volume 2777)
Cite this paper as:
Sloan R.H., Szörényi B., Turán G. (2003) Projective DNF Formulae and Their Revision. In: Schölkopf B., Warmuth M.K. (eds) Learning Theory and Kernel Machines. Lecture Notes in Computer Science, vol 2777. Springer, Berlin, Heidelberg


Valiant argued that biology imposes various constraints on learnability, and, motivated by these constraints, introduced his model of projection learning [14]. Projection learning aims to learn a target concept over some large domain, in this paper {0,1}n, by learning some of its projections to a class of smaller domains, and combining these projections. Valiant proved a general mistake bound for the resulting algorithm under certain conditions. The basic assumption underlying projection learning is that there is a family of simple projections that cover all positive instances of the target, where simple means belonging to some efficiently learnable class. The projections describing the target in this way can also be thought of as a set of experts, each specialized to classify a subset of the instances, such that whenever two experts overlap they always agree in their classification.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Robert H. Sloan
    • 1
  • Balázs Szörényi
    • 2
  • György Turán
    • 3
  1. 1.U. of Illinois at Chicago 
  2. 2.U. of SzegedHungary
  3. 3.U. of Illinois at Chicago and RGAI Hungarian Academy of Sciences 

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