Machine Learning Models and Algorithms for Big Data Classification T
This book presents machine learning models and algorithms to address big data classification problems. Existing machine learning techniques like the decision tree (a hierarchical approach), random forest (an ensemble hierarchical approach), and deep learn
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Shan Suthaharan
Machine Learning Models and Algorithms for Big Data Classification Thinking with Examples for Effective Learning
Integrated Series in Information Systems Volume 36
Series Editors Ramesh Sharda Oklahoma State University, Stillwater, OK, USA Stefan Voß University of Hamburg, Hamburg, Germany
More information about this series at http://www.springer.com/series/6157
Shan Suthaharan
Machine Learning Models and Algorithms for Big Data Classification Thinking with Examples for Effective Learning
123
Shan Suthaharan Department of Computer Science UNC Greensboro Greensboro, NC, USA
ISSN 1571-0270 ISSN 2197-7968 (electronic) Integrated Series in Information Systems ISBN 978-1-4899-7640-6 ISBN 978-1-4899-7641-3 (eBook) DOI 10.1007/978-1-4899-7641-3 Library of Congress Control Number: 2015950063 Springer New York Heidelberg Dordrecht London © Springer Science+Business Media New York 2016 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 respect to the material contained herein or for any errors or omissions that may have been made. Printed on acid-free paper Springer International Publishing AG Switzerland is part of Springer Science+Business Media (www. springer.com)
It is the quality of our work which will please God and not the quantity – Mahatma Gandhi If you can’t explain it simply, you don’t understand it well enough – Albert Einstein
Preface
The interest in writing this book began at the IEEE International Conference on Intelligence and Security Informatics held in Washington, DC (June 11–14, 2012), where Mr. Matthew Amboy, the editor of Business and Economics: OR and MS, published by Springer Science+Business Media, expressed the need for a book on this topic, mainly focusing on a topic in data science field. The interest went even deeper when I attended the workshop conducted by Professor Bin Yu (Department of Statistics, University of California, Berkeley) and Professor David Madigan (Department of Statistics, Columbia University) at the Institute for Mathematics and its Applications, University of Minnesota on June 16–29, 2013. Data science is one o
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