Problem Description
Mathematically, pattern recognition is a classification problem. Consider the recognition of characters. We wish to design a system such that a handwritten symbol will be recognized as an “A,” a “B,” etc. In other words, the machine we design must classify the observed handwritten character into one of 26 classes. The handwritten characters are often ambiguous, and there will be misclassified characters. The major goal in designing a pattern recognition machine is to have a low probability of misclassification.
There are many problems that can be formulated as pattern classification problems. For example, the weather may be divided into three classes, fair, rain, and possible rain, and the problem is to classify tomorrow’s weather into one of these three classes. In the recognition of electrocardiograms, the classes are disease categories plus the class of normal subjects. In binary data transmission, a “one” and a “zero” are represented by signals of amplitudes A...
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References and Further Reading
Abramowitz M, Stegun IA (1964) Handbook of mathematical functions. National Bureau of Standards, New York
Bishop CM (2006) Pattern recognition and machine learning. Springer, New York
Nechval NA (1992) Radar CFAR thresholding in clutter under detection of airborne birds. In: Proceedings of the 21st meeting of bird strike committee Europe. BSCE, Jerusalem, pp 127–140
Nechval NA (1997) Adaptive CFAR tests for detection of a signal in noise and deflection criterion. In: Wysocki T, Razavi H, Honary B (eds) Digital signal processing for communication systems. Kluwer, Boston, pp 177–186
Nechval NA, Nechval KN (1998) Recognition of applicant for project realization with good contract risk. In: Pranevicius H, Rapp B (eds) Organisational structures, management, simulation of business sectors and systems. Kaunas University of Technology, Lithuania, pp 70–72
Nechval NA, Nechval KN (1999) CFAR test for moving window detection of a signal in noise. In: Proceedings of the 5th international symposium on DSP for communication systems, Curtin University of Technology, Perth-Scarborough, pp 134–141
Nechval NA, Nechval KN, Srelchonok VF, Vasermanis EK (2004) Adaptive CFAR tests for detection and recognition of targets signals in radar clutter. In: Berger-Vachon C, Gil Lafuente AM (eds) The 2004 conferences best of. AMSE Periodicals, Barcelona, pp 62–80
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this entry
Cite this entry
Nechval, N.A., Nechval, K.N., Purgailis, M. (2011). Statistical Pattern Recognition Principles. In: Lovric, M. (eds) International Encyclopedia of Statistical Science. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04898-2_550
Download citation
DOI: https://doi.org/10.1007/978-3-642-04898-2_550
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04897-5
Online ISBN: 978-3-642-04898-2
eBook Packages: Mathematics and StatisticsReference Module Computer Science and Engineering