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Robustness in Statistical Pattern Recognition (Paperback, Softcover reprint of hardcover 1st ed. 1996)
Loot Price: R2,723
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Robustness in Statistical Pattern Recognition (Paperback, Softcover reprint of hardcover 1st ed. 1996)
Series: Mathematics and Its Applications, 380
Expected to ship within 18 - 22 working days
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This book is concerned with important problems of robust (stable)
statistical pat tern recognition when hypothetical model
assumptions about experimental data are violated (disturbed).
Pattern recognition theory is the field of applied mathematics in
which prin ciples and methods are constructed for classification
and identification of objects, phenomena, processes, situations,
and signals, i. e., of objects that can be specified by a finite
set of features, or properties characterizing the objects
(Mathematical Encyclopedia (1984)). Two stages in development of
the mathematical theory of pattern recognition may be observed. At
the first stage, until the middle of the 1970s, pattern recogni
tion theory was replenished mainly from adjacent mathematical
disciplines: mathe matical statistics, functional analysis,
discrete mathematics, and information theory. This development
stage is characterized by successful solution of pattern
recognition problems of different physical nature, but of the
simplest form in the sense of used mathematical models. One of the
main approaches to solve pattern recognition problems is the
statisti cal approach, which uses stochastic models of feature
variables. Under the statistical approach, the first stage of
pattern recognition theory development is characterized by the
assumption that the probability data model is known exactly or it
is esti mated from a representative sample of large size with
negligible estimation errors (Das Gupta, 1973, 1977), (Rey, 1978),
(Vasiljev, 1983))."
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