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Learning Decision Rules Empirically

  • Sándor Csibi
Part of the International Centre for Mechanical Sciences book series (CISM, volume 84)

Abstract

Assume a teacher presenting a sequence ηn = (ωn, dn),n = ... −1, 0,1,... of pairs of symptoms and associated diagnoses afteranother. Let ωn ∈ Ω (where Ω denotes, e.g., a subset of some M-variate Euclidean space). Admit dn = 0,1, where the value taken by dn declares whether, at instant n, some hypotheses H or its negation Hc actually holds.

Keywords

Decision Rule Reproduce Kernel Hilbert Space Positive Definite Function Optimal Decision Rule Finite Dimensional Estimate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Wien 1975

Authors and Affiliations

  • Sándor Csibi
    • 1
  1. 1.Telecom. Res. Inst.T.U. of BudapestBudapestHungary

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