Summary
Proposed is a unique approach to solving problema of automatic classification and pattern recognition. Described is a new class of decision functions, based on this approach, — taxonomic decision functions (TDF) possessing high stability against the breaking the representation law of training sampling.
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References
Zagoruiko N.G. (1981), Classification of forecast problems by the tables “object-property”, In: “Computer methods of discovering regularities” (Vychislitelnye sistemy, 88), Novosibirsk, p.3–8 (Russian).
Zagoruiko N.G. Recognition methods and their application. “Sovetskoje radio”, Moscow, 1972. 207 p. (Russian).
Yolkina V.N., Zagoruiko N.G. (1978), Some classification algorithms developed at Novosibirsk. R.A.I.R.O. Informatique/Computer Science. Vol. 12, No 1, p. 37–46.
Zagoruiko N.G. and Yolkina V.N. (1982), Inference and Data tables with missing values. Handbook of Statistics, vol. 2, North-Holland Publishing Company, p. 493–500.
Lbov G.S. (1982), Logical Function in the Problems of Empirical Prediction. Handbook of Statistics, vol. 2, North-Holland Publishing Company, p. 479–492.
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© 1984 Springer-Verlag Berlin Heidelberg
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Zagoruiko, N.G. (1984). Classification and Recognition. In: Havránek, T., Šidák, Z., Novák, M. (eds) Compstat 1984. Physica, Heidelberg. https://doi.org/10.1007/978-3-642-51883-6_24
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DOI: https://doi.org/10.1007/978-3-642-51883-6_24
Publisher Name: Physica, Heidelberg
Print ISBN: 978-3-7051-0007-7
Online ISBN: 978-3-642-51883-6
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