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 |
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Vienna
Springer Vienna
2002, 2002
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| Edition: | 1st ed. 2002 |
| Series: | CISM International Centre for Mechanical Sciences, Courses and Lectures
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| Collection: | Springer Book Archives -2004 - Collection details see MPG.ReNa |
| 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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| Physical Description: | V, 335 p online resource |
| ISBN: | 9783709125687 |