Lectures on the Nearest Neighbor Method
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 a
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Gérard Biau Luc Devroye
Lectures on the Nearest Neighbor Method
Springer Series in the Data Sciences Series Editors: Jianqing Fan, Princeton University Michael Jordan, University of California, Berkeley
Springer Series in the Data Sciences focuses primarily on monographs and graduate level textbooks. The target audience includes students and researchers working in and across the fields of mathematics, theoretical computer science, and statistics. Data Analysis and Interpretation is a broad field encompassing some of the fastestgrowing subjects in interdisciplinary statistics, mathematics and computer science. It encompasses a process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions, and supporting decision making. Data analysis has multiple facets and approaches, including diverse techniques under a variety of names, in different business, science, and social science domains. Springer Series in the Data Sciences addresses the needs of a broad spectrum of scientists and students who are utilizing quantitative methods in their daily research. The series is broad but structured, including topics within all core areas of the data sciences. The breadth of the series reflects the variation of scholarly projects currently underway in the field of machine learning.
More information about this series at http://www.springer.com/series/13852
Gérard Biau • Luc Devroye
Lectures on the Nearest Neighbor Method
123
Gérard Biau Université Pierre et Marie Curie Paris, France
Luc Devroye McGill University Montreal, Quebec, Canada
ISSN 2365-5674 ISSN 2365-5682 (electronic) Springer Series in the Data Sciences ISBN 978-3-319-25386-2 ISBN 978-3-319-25388-6 (eBook) DOI 10.1007/978-3-319-25388-6 Library of Congress Control Number: 2015956603 Mathematics Subject Classification (2010): 62G05, 62G07, 62G08, 62G20, 62G30, 62H30, 68T05, 68T10, 60C05, 60D05 Springer Cham Heidelberg New York Dordrecht London © Springer International Publishing Switzerland 2015 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with r
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