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...
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| Format: | eBook |
| Language: | English |
| Published: |
Wien ; New York :
Springer,
[2002]
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| Series: | Courses and lectures ;
no. 434. |
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| Online Access: | Connect to the full text of this electronic book |
| 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. |
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| Item Description: | Electronic resource. |
| Physical Description: | 1 online resource (335 pages) |
| Format: | Master and use copy. Digital master created according to Benchmark for Faithful Digital Reproductions of Monographs and Serials, Version 1. Digital Library Federation, December 2002. |
| Bibliography: | Includes bibliographical references. |
| ISBN: | 9783709125687 (electronic bk.) 3709125685 (electronic bk.) |