Principles of Nonparametric Learning:
The book provides systematic in-depth analysis of nonparametric learning. It covers the theoretical limits and the asymptotical optimal algorithms and estimates, such as pattern recognition, nonparametric regression estimation, universal prediction, vector quantization, distribution and density esti...
Saved in:
| Other Authors: | |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Vienna
Springer Vienna
2002
|
| Series: | International Centre for Mechanical Sciences, Courses and Lectures
434 |
| Subjects: | |
| Online Access: | FHI01 BTU01 Volltext |
| Summary: | The book provides systematic in-depth analysis of nonparametric learning. It covers the theoretical limits and the asymptotical optimal algorithms and estimates, such as pattern recognition, nonparametric regression estimation, universal prediction, vector quantization, distribution and density estimation and genetic programming. The book is mainly addressed to postgraduates in engineering, mathematics, computer science, and researchers in universities and research institutions |
| Physical Description: | 1 Online-Ressource (V, 335 p) |
| ISBN: | 9783709125687 |
| DOI: | 10.1007/978-3-7091-2568-7 |
Staff View
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Record in the Search Index
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| discipline | Informatik Elektrotechnik / Elektronik / Nachrichtentechnik |
| doi_str_mv | 10.1007/978-3-7091-2568-7 |
| format | Electronic eBook |
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| illustrated | Not Illustrated |
| indexdate | 2024-07-10T08:10:04Z |
| institution | BVB |
| isbn | 9783709125687 |
| language | English |
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| physical | 1 Online-Ressource (V, 335 p) |
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| publisher | Springer Vienna |
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| series2 | International Centre for Mechanical Sciences, Courses and Lectures |
| spelling | Principles of Nonparametric Learning edited by László Györfi Vienna Springer Vienna 2002 1 Online-Ressource (V, 335 p) txt rdacontent c rdamedia cr rdacarrier International Centre for Mechanical Sciences, Courses and Lectures 434 The book provides systematic in-depth analysis of nonparametric learning. It covers the theoretical limits and the asymptotical optimal algorithms and estimates, such as pattern recognition, nonparametric regression estimation, universal prediction, vector quantization, distribution and density estimation and genetic programming. The book is mainly addressed to postgraduates in engineering, mathematics, computer science, and researchers in universities and research institutions Engineering Signal, Image and Speech Processing Probability and Statistics in Computer Science Pattern Recognition Statistical Theory and Methods Mathematical statistics Pattern recognition Statistics Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Nichtparametrisches Verfahren (DE-588)4339273-8 gnd rswk-swf 1\p (DE-588)1071861417 Konferenzschrift 2001 Udine gnd-content Maschinelles Lernen (DE-588)4193754-5 s Nichtparametrisches Verfahren (DE-588)4339273-8 s 2\p DE-604 Györfi, László edt Erscheint auch als Druck-Ausgabe 9783211836880 https://doi.org/10.1007/978-3-7091-2568-7 Verlag URL des Erstveröffentlichers Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 2\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Principles of Nonparametric Learning Engineering Signal, Image and Speech Processing Probability and Statistics in Computer Science Pattern Recognition Statistical Theory and Methods Mathematical statistics Pattern recognition Statistics Maschinelles Lernen (DE-588)4193754-5 gnd Nichtparametrisches Verfahren (DE-588)4339273-8 gnd |
| subject_GND | (DE-588)4193754-5 (DE-588)4339273-8 (DE-588)1071861417 |
| title | Principles of Nonparametric Learning |
| title_auth | Principles of Nonparametric Learning |
| title_exact_search | Principles of Nonparametric Learning |
| title_full | Principles of Nonparametric Learning edited by László Györfi |
| title_fullStr | Principles of Nonparametric Learning edited by László Györfi |
| title_full_unstemmed | Principles of Nonparametric Learning edited by László Györfi |
| title_short | Principles of Nonparametric Learning |
| title_sort | principles of nonparametric learning |
| topic | Engineering Signal, Image and Speech Processing Probability and Statistics in Computer Science Pattern Recognition Statistical Theory and Methods Mathematical statistics Pattern recognition Statistics Maschinelles Lernen (DE-588)4193754-5 gnd Nichtparametrisches Verfahren (DE-588)4339273-8 gnd |
| topic_facet | Engineering Signal, Image and Speech Processing Probability and Statistics in Computer Science Pattern Recognition Statistical Theory and Methods Mathematical statistics Pattern recognition Statistics Maschinelles Lernen Nichtparametrisches Verfahren Konferenzschrift 2001 Udine |
| url | https://doi.org/10.1007/978-3-7091-2568-7 |
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