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Mohssen Mohammed Muhammad Badruddin Khan - Machine Learning

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As the complexity of todays networked computer systems grows, they become increasingly difficult to understand, predict, and control. Addressing these challenges requires new approaches to building these systems.Adaptive, Dynamic, and Resilient Systemssupplies readers with various perspectives of the critical infrastructure that systems of networked computers rely on. It introduces the key issues, describes their interrelationships, and presents new research in support of these areas.
The book presents the insights of a different group of international experts in each chapter. Reporting on recent developments in adaptive systems, it begins with a survey of application fields. It explains the requirements of such fields in terms of adaptation and resilience. It also provides some abstract relationship graphs that illustrate the key attributes of distributed systems to supply you with a better understanding of these factors and their dependencies.
The text examines resilient adaptive systems from the perspectives of mobile, infrastructure, and enterprise systems and protecting critical infrastructure. It details various approaches for building adaptive, dynamic, and resilient systems--including agile, grid, and autonomic computing; multi-agent-based and biologically inspired approaches; and self-organizing systems.
The book includes many stories of successful applications that illustrate a diversified range of cutting-edge approaches. It concludes by covering related topics and techniques that can help to boost adaptation and resilience in your systems.

Mohssen Mohammed Muhammad Badruddin Khan: author's other books


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Machine Learning Algorithms and Applications OTHER TITLES FROM AUERBACH - photo 1

Machine Learning

Algorithms and Applications

OTHER TITLES FROM AUERBACH PUBLICATIONS AND CRC PRESS

Adaptive, Dynamic, and Resilient Systems

Edited by Niranjan Suri and
Giacomo Cabri
ISBN 978-1-4398-6848-5

Anti-Spam Techniques Based on Artificial Immune System

Ying Tan
ISBN 978-1-4987-2518-7

Case Studies in Secure Computing: Achievements and Trends

Edited by Biju Issac
and Nauman Israr
ISBN 978-1-4822-0706-4

Cognitive Robotics

Edited by Hooman Samani
ISBN 978-1-4822-4456-4

Computational Intelligent Data Analysis for Sustainable Development

Edited by Ting Yu, Nitesh Chawla,
and Simeon Simoff
ISBN 978-1-4398-9594-8

Computational Trust Models and Machine Learning

Xin Liu, Anwitaman Datta,
and Ee-Peng Lim
ISBN 978-1-4822-2666-9

Enhancing Computer Security with Smart Technology

V. Rao Vemuri
ISBN 978-0-8493-3045-2

Exploring Neural Networks with C#

Ryszard Tadeusiewicz,
Rituparna Chaki, and
Nabendu Chaki
ISBN 978-1-4822-3339-1

Generic and Energy-Efficient Context-Aware Mobile Sensing

Ozgur Yurur and Chi Harold Liu
ISBN 978-1-4987-0010-8

Network Anomaly Detection: A Machine Learning Perspective

Dhruba Kumar Bhattacharyya
and Jugal Kumar Kalita
ISBN 978-1-4665-8208-8

Risks of Artificial Intelligence

Vincent C. Mller
ISBN 978-1-4987-3482-0

The Cognitive Early Warning Predictive System Using the Smart Vaccine: The New Digital Immunity Paradigm for Smart Cities and Critical Infrastructure

Rocky Termanini
ISBN 978-1-4987-2651-1

The State of the Art in Intrusion Prevention and Detection

Edited by Al-Sakib Khan Pathan
ISBN 978-1-4822-0351-6

Zeroing Dynamics, Gradient Dynamics, and Newton Iterations

Yunong Zhang, Lin Xiao,
Zhengli Xiao, and Mingzhi Mao
ISBN 978-1-4987-5376-0

Machine Learning

Algorithms and Applications

Mohssen Mohammed

Muhammad Badruddin Khan

Eihab Bashier Mohammed Bashier

Contents Landmarks MATLAB is a trademark of The MathWorks Inc and is used - photo 2

Contents
Landmarks

MATLAB is a trademark of The MathWorks, Inc. and is used with permission. The MathWorks does not warrant the accuracy of the text or exercises in this book. This books use or discussion of MATLAB software or related products does not constitute endorsement or sponsorship by The MathWorks of a particular pedagogical approach or particular use of the MATLAB software.

CRC Press
Taylor & Francis Group
6000 Broken Sound Parkway NW, Suite 300
Boca Raton, FL 33487-2742

2017 by Taylor & Francis Group, LLC
CRC Press is an imprint of Taylor & Francis Group, an Informa business

No claim to original U.S. Government works

Printed on acid-free paper
Version Date: 20160428

International Standard Book Number-13: 978-1-4987-0538-7 (Hardback)

This book contains information obtained from authentic and highly regarded sources. Reasonable efforts have been made to publish reliable data and information, but the author and publisher cannot assume responsibility for the validity of all materials or the consequences of their use. The authors and publishers have attempted to trace the copyright holders of all material reproduced in this publication and apologize to copyright holders if permission to publish in this form has not been obtained. If any copyright material has not been acknowledged please write and let us know so we may rectify in any future reprint.

Except as permitted under U.S. Copyright Law, no part of this book may be reprinted, reproduced, transmitted, or utilized in any form by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying, microfilming, and recording, or in any information storage or retrieval system, without written permission from the publishers.

For permission to photocopy or use material electronically from this work, please access www.copyright.com (http://www.copyright.com/) or contact the Copyright Clearance Center, Inc. (CCC), 222 Rosewood Drive, Danvers, MA 01923, 978-750-8400. CCC is a not-for-profit organization that provides licenses and registration for a variety of users. For organizations that have been granted a photocopy license by the CCC, a separate system of payment has been arranged.

Trademark Notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation without intent to infringe.

Library of Congress Cataloging-in-Publication Data

Names: Mohammed, Mohssen, 1982- author. | Khan, Muhammad Badruddin, author. |

Bashier, Eihab Bashier Mohammed, author.

Title: Machine learning : algorithms and applications / Mohssen Mohammed,

Muhammad Badruddin Khan, and Eihab Bashier Mohammed Bashier.

Description: Boca Raton : CRC Press, 2017. | Includes bibliographical

references and index.

Identifiers: LCCN 2016015290 | ISBN 9781498705387 (hardcover : alk. paper)

Subjects: LCSH: Machine learning. | Computer algorithms.

Classification: LCC Q325.5 .M63 2017 | DDC 006.3/12--dc23

LC record available at https://lccn.loc.gov/2016015290

Visit the Taylor & Francis Web site at
http://www.taylorandfrancis.com

and the CRC Press Web site at
http://www.crcpress.com

To our parents, families, brothers and sisters, and
to our students, we dedicate this book.

If you are new to machine learning and you do not know which book to start from, then the answer is this book. If you know some of the theories in machine learning, but you do not know how to write your own algorithms, then again you should start from this book.

This book focuses on the supervised and unsupervised machine learning methods. The main objective of this book is to introduce these methods in a simple and practical way, so that they can be understood even by beginners to get benefit from them.

In each chapter, we discuss the algorithms through which the chapter methods work, and implement the algorithms in MATLAB. We chose MATLAB to be the main programming language of the book because it is simple and widely used among scientists; at the same time, it supports the machine learning methods through its statistics toolbox.

The book consists of 12 chapters, divided into two sections:

I: Supervised Learning Algorithms

II: Unsupervised Learning Algorithms

In the first section, we discuss the decision trees, rule-based classifiers, nave Bayes classification, k-nearest neighbors, neural networks, linear discriminant analysis, and support vector machines.

In the second section, we discuss the k-means, Gaussian mixture model, hidden Markov model, and principal component analysis in the context of dimensionality reduction.

We have written the chapters in such a way that all are independent of one another. That means the reader can start from any chapter and understand it easily.

MATLAB is a registered trademark of The MathWorks, Inc. For product information, please contact:

The MathWorks, Inc.

3 Apple Hill Drive

Natick, MA 01760-2098 USA

Tel: 508-647-7000

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