Robustness in Statistical Pattern Recognition:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Dordrecht
Springer Netherlands
1996
|
| Schriftenreihe: | Mathematics and Its Applications
380 |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Beschreibung: | This book is concerned with important problems of robust (stable) statistical pattern recognition when hypothetical model assumptions about experimental data are violated (disturbed). Pattern recognition theory is the field of applied mathematics in which principles 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 recognition theory was replenished mainly from adjacent mathematical disciplines: mathematical 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 statistical 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 estimated from a representative sample of large size with negligible estimation errors (Das Gupta, 1973, 1977), (Rey, 1978), (Vasiljev, 1983)) |
| Beschreibung: | 1 Online-Ressource (XIV, 302 p) |
| ISBN: | 9789401586306 9789048147601 |
| DOI: | 10.1007/978-94-015-8630-6 |
Internformat
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| 490 | 1 | |a Mathematics and Its Applications |v 380 | |
| 500 | |a This book is concerned with important problems of robust (stable) statistical pattern recognition when hypothetical model assumptions about experimental data are violated (disturbed). Pattern recognition theory is the field of applied mathematics in which principles 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 recognition theory was replenished mainly from adjacent mathematical disciplines: mathematical 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 statistical 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 estimated from a representative sample of large size with negligible estimation errors (Das Gupta, 1973, 1977), (Rey, 1978), (Vasiljev, 1983)) | ||
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Datensatz im Suchindex
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| dewey-full | 519.5 |
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| discipline | Mathematik |
| doi_str_mv | 10.1007/978-94-015-8630-6 |
| format | Electronic eBook |
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| isbn | 9789401586306 9789048147601 |
| language | English |
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| spelling | Kharin, Yurij S. Verfasser (DE-588)17180645X aut Robustness in Statistical Pattern Recognition by Yurij Kharin Dordrecht Springer Netherlands 1996 1 Online-Ressource (XIV, 302 p) txt rdacontent c rdamedia cr rdacarrier Mathematics and Its Applications 380 This book is concerned with important problems of robust (stable) statistical pattern recognition when hypothetical model assumptions about experimental data are violated (disturbed). Pattern recognition theory is the field of applied mathematics in which principles 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 recognition theory was replenished mainly from adjacent mathematical disciplines: mathematical 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 statistical 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 estimated from a representative sample of large size with negligible estimation errors (Das Gupta, 1973, 1977), (Rey, 1978), (Vasiljev, 1983)) Statistics Artificial intelligence Mathematics Statistics, general Applications of Mathematics Artificial Intelligence (incl. Robotics) Signal, Image and Speech Processing Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences Künstliche Intelligenz Mathematik Statistik Robustheit (DE-588)4126481-2 gnd rswk-swf Mustererkennung (DE-588)4040936-3 gnd rswk-swf Mustererkennung (DE-588)4040936-3 s Robustheit (DE-588)4126481-2 s 1\p DE-604 Mathematics and Its Applications 380 (DE-604)BV008163334 380 https://doi.org/10.1007/978-94-015-8630-6 Verlag Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Kharin, Yurij S. Robustness in Statistical Pattern Recognition Mathematics and Its Applications Statistics Artificial intelligence Mathematics Statistics, general Applications of Mathematics Artificial Intelligence (incl. Robotics) Signal, Image and Speech Processing Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences Künstliche Intelligenz Mathematik Statistik Robustheit (DE-588)4126481-2 gnd Mustererkennung (DE-588)4040936-3 gnd |
| subject_GND | (DE-588)4126481-2 (DE-588)4040936-3 |
| title | Robustness in Statistical Pattern Recognition |
| title_auth | Robustness in Statistical Pattern Recognition |
| title_exact_search | Robustness in Statistical Pattern Recognition |
| title_full | Robustness in Statistical Pattern Recognition by Yurij Kharin |
| title_fullStr | Robustness in Statistical Pattern Recognition by Yurij Kharin |
| title_full_unstemmed | Robustness in Statistical Pattern Recognition by Yurij Kharin |
| title_short | Robustness in Statistical Pattern Recognition |
| title_sort | robustness in statistical pattern recognition |
| topic | Statistics Artificial intelligence Mathematics Statistics, general Applications of Mathematics Artificial Intelligence (incl. Robotics) Signal, Image and Speech Processing Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences Künstliche Intelligenz Mathematik Statistik Robustheit (DE-588)4126481-2 gnd Mustererkennung (DE-588)4040936-3 gnd |
| topic_facet | Statistics Artificial intelligence Mathematics Statistics, general Applications of Mathematics Artificial Intelligence (incl. Robotics) Signal, Image and Speech Processing Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences Künstliche Intelligenz Mathematik Statistik Robustheit Mustererkennung |
| url | https://doi.org/10.1007/978-94-015-8630-6 |
| volume_link | (DE-604)BV008163334 |
| work_keys_str_mv | AT kharinyurijs robustnessinstatisticalpatternrecognition |