Abstract
The development of various areas of science and technology that substantially change human possibilities passes almost all the time through the following three stages.
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Nilsson, N. (1965). Learning machine: Foundation of trainable pattern recognition classifying systems. McGraw-Hill, New York.
Fisher, R. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7, Part II: 179–188.
Waerden, B. v. d. (1957). Mathematische Statistik; in German(Mathematical statistics). Springer-Verlag, Berlin-Goettingen-Heidelberg.
Raudys, S. and Pikelis, V. (1980). On dimensionality, sample size, classification error, and complexity of classification algorithm in pattern recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1: 7–13.
Schlesinger, M. (1965). 0 samoproizvolnom razlichenii obrazov; in Russian (On automatic separation of patterns). In Chitajushchie avtomaty (Reading Automata), pages 38–45. Naukova Dumka, Kiev.
Schlesinger, M. (1968). Vzaimosvjaz obuchenija i samoobuchenija v raspoznavaniji obrazov; in Russian (Relation between learning and self-learning in pattern recognition). Kibernetika, (2):81–88.
Markov, A. (1916). Ob odnom primenenii statisticeskogo metoda; in Russian (An application of statistical method). Izvestia imperialisticeskoj akademii nauk, 6 (4): 239–242.
Minsky, M. and Papert, S. (1969). Perceptrons: An introduction to computational geometry. MIT Press, Cambridge, Mass., USA. 2nd edition in 1988.
Neyman, J. and Pearson, E. (1933). On the problem of the most efficient tests of statistical hypotheses. Phil. Trans. Royal Soc. London, 231: 289–337.
Pavel, M. (1993). Fundamentals of pattern recognition. Marcel Dekker, Inc., New York, USA.
Vidyasagar, M. (1996). Theory of Learning and Generalization. With Application to Neural Networks and Control Systems. Springer.
Zagorujko, N. (1999). Prikladnyje metoda nalaliza danych i znanij; in Russian (Applied methods of data and knowledge analysis). Izdatelstvo Instituta Matematiki, Novosibirsk, Russia.
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Schlesinger, M.I., Hlaváč, V. (2002). Learning in pattern recognition. In: Ten Lectures on Statistical and Structural Pattern Recognition. Computational Imaging and Vision, vol 24. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-3217-8_4
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DOI: https://doi.org/10.1007/978-94-017-3217-8_4
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