Machine Learning_Algorithms and Applications (True PDF)(2017).pdf

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Cover

Half Title

Title Page

Contents

Preface

Acknowledgments

Authors

Introduction

1 Introduction to Machine Learning

1.1 Introduction

1.2 Preliminaries

1.2.1 Machine Learning: Where Several Disciplines Meet

1.2.2 Supervised Learning

1.2.3 Unsupervised Learning

1.2.4 Semi-Supervised Learning

1.2.5 Reinforcement Learning

1.2.6 Validation and Evaluation

1.3 Applications of Machine Learning Algorithms

1.3.1 Automatic Recognition of Handwritten Postal Codes

1.3.2 Computer-Aided Diagnosis

1.3.3 Computer Vision

1.3.3.1 Driverless Cars

1.3.3.2 Face Recognition and Security

1.3.4 Speech Recognition

1.3.5 Text Mining

1.3.5.1 Where Text and Image Data Can Be Used Together

1.4 The Present and the Future

1.4.1 Thinking Machines

1.4.2 Smart Machines

1.4.3 Deep Blue

1.4.4 IBM’s Watson

1.4.5 Google Now

1.4.6 Apple’s Siri

1.4.7 Microsoft’s Cortana

1.5 Objective of This Book

References

SECTION I: SUPERVISED LEARNING ALGORITHMS

2 Decision Trees

2.1 Introduction

2.2 Entropy

2.2.1 Example

2.2.2 Understanding the Concept of Number of Bits

2.3 Attribute Selection Measure

2.3.1 Information Gain of ID3

2.3.2 The Problem with Information Gain

2.4 Implementation in MATLAB[sup(®)]

2.4.1 Gain Ratio of C4.5

2.4.2 Implementation in MATLAB

References

3 Rule-Based Classifiers

3.1 Introduction to Rule-Based Classifiers

3.2 Sequential Covering Algorithm

3.3 Algorithm

3.4 Visualization

3.5 Ripper

3.5.1 Algorithm

3.5.2 Understanding Rule Growing Process

3.5.3 Information Gain

3.5.4 Pruning

3.5.5 Optimization

References

4 Naïve Bayesian Classification

4.1 Introduction

4.2 Example

4.3 Prior Probability

4.4 Likelihood

4.5 Laplace Estimator

4.6 Posterior Probability

4.7 MATLAB Implementation

References

5 The k-Nearest Neighbors Classifiers

5.1 Introduction

5.2 Example

5.3 k-Nearest Neighbors in MATLAB[sup(®)]

References

6 Neural Networks

6.1 Perceptron Neural Network

6.1.1 Perceptrons

6.2 MATLAB Implementation of the Perceptron Training and Testing Algorithms

6.3 Multilayer Perceptron Networks

6.4 The Backpropagation Algorithm

6.4.1 Weights Updates in Neural Networks

6.5 Neural Networks in MATLAB

References

7 Linear Discriminant Analysis

7.1 Introduction

7.2 Example

References

8 Support Vector Machine

8.1 Introduction

8.2 Definition of the Problem

8.2.1 Design of the SVM

8.2.2 The Case of Nonlinear Kernel

8.3 The SVM in MATLAB[sup(®)]

References

SECTION II: UNSUPERVISED LEARNING ALGORITHMS

9 k-Means Clustering

9.1 Introduction

9.2 Description of the Method

9.3 The k-Means Clustering Algorithm

9.4 The k-Means Clustering in MATLAB[sup(®)]

10 Gaussian Mixture Model

10.1 Introduction

10.2 Learning the Concept by Example

References

11 Hidden Markov Model

11.1 Introduction

11.2 Example

11.3 MATLAB Code

References

12 Principal Component Analysis

12.1 Introduction

12.2 Description of the Problem

12.3 The Idea behind the PCA

12.3.1 The SVD and Dimensionality Reduction

12.4 PCA Implementation

12.4.1 Number of Principal Components to Choose

12.4.2 Data Reconstruction Error

12.5 The Following MATLAB[sup(®)] Code Applies the PCA

12.6 Principal Component Methods in Weka

12.7 Example: Polymorphic Worms Detection Using PCA

12.7.1 Introduction

12.7.2 SEA, MKMP, and PCA

12.7.3 Overview and Motivation for Using String Matching

12.7.4 The KMP Algorithm

12.7.5 Proposed SEA

12.7.6 An MKMP Algorithm

12.7.6.1 Testing the Quality of the Generated Signature for Polymorphic Worm A

12.7.7 A Modified Principal Component Analysis

12.7.7.1 Our Contributions in the PCA

12.7.7.2 Testing the Quality of Generated Signature for Polymorphic Worm A

12.7.7.3 Clustering Method for Different Types of Polymorphic Worms

12.7.8 Signature Generation Algorithms Pseudo-Codes

12.7.8.1 Signature Generation Process

References

Appendix I: Transcript of Conversations with Chatbot

Appendix II: Creative Chatbot

Index

Machine Learning Algorithms and Applications

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Machine Learning Algorithms and Applications Mohssen Mohammed Muhammad Badruddin Khan Eihab Bashier Mohammed Bashier CRC CRC Press Taylor & Francis Group Boca Raton London New York CRC Press is an imprint of the Taylor & Francis Group, an informa business

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 book’s use or discussion of MATLAB® soft- ware 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, transmit- ted, 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.

Contents Preface ................................................................................xiii Acknowledgments ............................................................. xv Authors .............................................................................. xvii Introduction ...................................................................... xix 1 Introduction to Machine Learning...........................1 1.1 Introduction ................................................................ 1 1.2 Preliminaries ............................................................... 2 1.2.1 Machine Learning: Where Several Disciplines Meet ............................................... 4 1.2.2 Supervised Learning ........................................ 7 1.2.3 Unsupervised Learning .................................... 9 1.2.4 Semi-Supervised Learning ..............................10 1.2.5 Reinforcement Learning ..................................11 1.2.6 Validation and Evaluation ...............................11 1.3 Applications of Machine Learning Algorithms .........14 1.3.1 Automatic Recognition of Handwritten Postal Codes ....................................................15 1.3.2 Computer-Aided Diagnosis .............................17 1.3.3 Computer Vision .............................................19 1.3.3.1 Driverless Cars ....................................20 1.3.3.2 Face Recognition and Security ...........22 1.3.4 Speech Recognition ........................................22 vii

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