Authors:
- Contains both background information and much more sophisticated material on kernel density estimation (KDE), its computational aspects, and its applications
- Describes in detail computational-like problems related to KDE
- Includes R source codes for replicating all the figures included in the book—making it a good source for newcomers to the field
- Includes supplementary material: sn.pub/extras
Part of the book series: Studies in Big Data (SBD, volume 37)
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Table of contents (8 chapters)
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Front Matter
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Back Matter
About this book
This book describes computational problems related to kernel density estimation (KDE) – one of the most important and widely used data smoothing techniques. A very detailed description of novel FFT-based algorithms for both KDE computations and bandwidth selection are presented.
The theory of KDE appears to have matured and is now well developed and understood. However, there is not much progress observed in terms of performance improvements. This book is an attempt to remedy this.
The book primarily addresses researchers and advanced graduate or postgraduate students who are interested in KDE and its computational aspects. The book contains both some background and much more sophisticated material, hence also more experienced researchers in the KDE area may find it interesting.
The presented material is richly illustrated with many numerical examples using both artificial and real datasets. Also, a number of practical applications related to KDE are presented.
Authors and Affiliations
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Institute of Control and Computation Engineering, University of Zielona Góra , Zielona Góra, Poland
Artur Gramacki
About the author
Bibliographic Information
Book Title: Nonparametric Kernel Density Estimation and Its Computational Aspects
Authors: Artur Gramacki
Series Title: Studies in Big Data
DOI: https://doi.org/10.1007/978-3-319-71688-6
Publisher: Springer Cham
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer International Publishing AG 2018
Hardcover ISBN: 978-3-319-71687-9Published: 22 January 2018
Softcover ISBN: 978-3-319-89094-4Published: 04 June 2019
eBook ISBN: 978-3-319-71688-6Published: 21 December 2017
Series ISSN: 2197-6503
Series E-ISSN: 2197-6511
Edition Number: 1
Number of Pages: XXIX, 176
Number of Illustrations: 70 b/w illustrations
Topics: Computational Intelligence, Artificial Intelligence, Big Data