Abstract
Pattern recognition or discrimination is about guessing or predicting the unknown nature of an observation, a discrete quantity such as black or white, one or zero, sick or healthy, real or fake. An observation is a collection of numerical measurements such as an image (which is a sequence of bits, one per pixel), a vector of weather data, an electrocardiogram, or a signature on a check suitably digitized. More formally, an observation is a d-dimensional vector x. The unknown nature of the observation is called a class. It is denoted by y and takes values in a finite set {1, 2,..., M}. In pattern recognition, one creates a function g(x): ℛd → {1,..., M} which represents one’s guess of y given x. The mapping g is called a classifier. Our classifier errs on x if g(x) ≠ y.
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© 1996 Springer Science+Business Media New York
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Devroye, L., Györfi, L., Lugosi, G. (1996). Introduction. In: A Probabilistic Theory of Pattern Recognition. Stochastic Modelling and Applied Probability, vol 31. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-0711-5_1
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DOI: https://doi.org/10.1007/978-1-4612-0711-5_1
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4612-6877-2
Online ISBN: 978-1-4612-0711-5
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