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.)
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 1975 Springer-Verlag Wien
About this chapter
Cite this chapter
Csibi, S. (1975). Basic Notions. In: Stochastic Processes with Learning Properties. International Centre for Mechanical Sciences, vol 84. Springer, Vienna. https://doi.org/10.1007/978-3-7091-3006-3_1
Download citation
DOI: https://doi.org/10.1007/978-3-7091-3006-3_1
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-81337-9
Online ISBN: 978-3-7091-3006-3
eBook Packages: Springer Book Archive