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...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Weitere beteiligte Personen: Györfi, László (HerausgeberIn)
Format: Elektronisch E-Book
Sprache:Englisch
Veröffentlicht: Vienna Springer Vienna 2002
Schriftenreihe:International Centre for Mechanical Sciences, Courses and Lectures 434
Schlagwörter:
Links:https://doi.org/10.1007/978-3-7091-2568-7
https://doi.org/10.1007/978-3-7091-2568-7
https://doi.org/10.1007/978-3-7091-2568-7
Zusammenfassung: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
Umfang:1 Online-Ressource (V, 335 p)
ISBN:9783709125687
DOI:10.1007/978-3-7091-2568-7