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Basic Notions

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

Abstract

Many of the problems, what one likes to term in Computer Science machine learning, may be formulated as follows: Assume samples ωi, i=0,1,... drawn after another from some set Ω, which is, say, an Euclidean space or some subset of it. Members ω∈ω may directly characterize results of actual measurements, observations, objects or symptoms. However, it is more appropriate to think of an Ω that is the collection of features we have derived from such data by means of some appropriate many-to-one mapping. (Many of the interesting and crucial techniques of pattern recognition are concerned just with such feature extraction procedures. However, in what follows, the actual interpretation of Ω does not make much matter.)

Keywords

Machine Learn Learning Problem Decision Function Basic Notion Target Quantity 
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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