Abstract
Statistical decision making (SDM) [1] and artificial neural networks (NN) [2] support the same activity, namely, decision making. In this chapter, decision making is understood in a wide sense that covers pattern recognition, cluster analysis, parameter estimation, prediction, diagnostics, fault detection, control design etc. In any of these tasks, the available information is processed in order to make some action: to assign a proper class to an observed sample, to guess what values an unobserved quantity may have, to predict what values of some quantities will occur, to guess the state of a patient or a technical system, to select values of manipulable variables fed into a controlled system etc.
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Kárný, M., Warwick, K., Kůrková, V. (1998). Statistical Decision Making and Neural Networks. In: Kárný, M., Warwick, K., Kůrková, V. (eds) Dealing with Complexity. Perspectives in Neural Computing. Springer, London. https://doi.org/10.1007/978-1-4471-1523-6_3
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DOI: https://doi.org/10.1007/978-1-4471-1523-6_3
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