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Index
Cover
Half Title
Title Page
Copyright Page
Dedication
Table of 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®
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®
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®
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®
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® 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
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