Overview
- Presents a rigorous overview of nearest neighbor methods
- Many different components covered: statistical, probabilistic, combinatorial, and geometric ideas
- Extensive appendix material provided
Part of the book series: Springer Series in the Data Sciences (SSDS)
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Table of contents (20 chapters)
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Front Matter
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Regression estimation
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Front Matter
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Supervised classification
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Front Matter
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Keywords
About this book
This text presents a wide-ranging and rigorous overview of nearest neighbor methods, one of the most important paradigms in machine learning. Now in one self-contained volume, this book systematically covers key statistical, probabilistic, combinatorial and geometric ideas for understanding, analyzing and developing nearest neighbor methods.
Gérard Biau is a professor at Université Pierre et Marie Curie (Paris). Luc Devroye is a professor at the School of Computer Science at McGill University (Montreal).
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Authors and Affiliations
Bibliographic Information
Book Title: Lectures on the Nearest Neighbor Method
Authors: Gérard Biau, Luc Devroye
Series Title: Springer Series in the Data Sciences
DOI: https://doi.org/10.1007/978-3-319-25388-6
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer International Publishing Switzerland 2015
Hardcover ISBN: 978-3-319-25386-2Published: 15 December 2015
Softcover ISBN: 978-3-319-79782-3Published: 21 March 2019
eBook ISBN: 978-3-319-25388-6Published: 08 December 2015
Series ISSN: 2365-5674
Series E-ISSN: 2365-5682
Edition Number: 1
Number of Pages: IX, 290
Number of Illustrations: 4 illustrations in colour
Topics: Probability Theory and Stochastic Processes, Pattern Recognition, Statistics and Computing/Statistics Programs