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Principles of Nonparametric Learning
Laszlo . Ed(S): Gyorfi
€ 183.99
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Description for Principles of Nonparametric Learning
Paperback. Provides an analysis of nonparametric learning. This title 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. Editor(s): Gyorfi, Laszlo. Series: CISM International Centre for Mechanical Sciences. Num Pages: 340 pages, biography. BIC Classification: PBT; UYQ. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly. Dimension: 244 x 170 x 19. Weight in Grams: 600.
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.
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.
Product Details
Format
Paperback
Publication date
2002
Publisher
Springer Verlag GmbH Austria
Number of pages
340
Condition
New
Series
CISM International Centre for Mechanical Sciences
Number of Pages
335
Place of Publication
Vienna, Austria
ISBN
9783211836880
SKU
V9783211836880
Shipping Time
Usually ships in 15 to 20 working days
Ref
99-15
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