book

Machine Learning

Name: Machine Learning
ISBN: 9781315354415

by Mohssen Mohammed, Muhammad Badruddin Khan, Eihab Mohammed Bashier

August 2016

Intermediate to advanced

204 pages

3h 51m

English

CRC Press

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Cover
Half Title
Title Page
Copyright Page
Table of Contents
Dedication
Preface
Acknowledgments
Authors
Introduction

1 Introduction to Machine Learning
1.1 Introduction1.2 Preliminaries1.2.1 Machine Learning: Where Several Disciplines Meet1.2.2 Supervised Learning1.2.3 Unsupervised Learning1.2.4 Semi-Supervised Learning1.2.5 Reinforcement Learning1.2.6 Validation and Evaluation1.3 Applications of Machine Learning Algorithms1.3.1 Automatic Recognition of Handwritten Postal Codes1.3.2 Computer-Aided Diagnosis1.3.3 Computer Vision1.3.3.1 Driverless Cars1.3.3.2 Face Recognition and Security1.3.4 Speech Recognition1.3.5 Text Mining1.3.5.1 Where Text and Image Data Can Be Used Together1.4 The Present and the Future1.4.1 Thinking Machines1.4.2 Smart Machines1.4.3 Deep Blue1.4.4 IBM’s Watson1.4.5 Google Now1.4.6 Apple’s Siri1.4.7 Microsoft’s Cortana1.5 Objective of This BookReferences
SECTION I SUPERVISED LEARNING ALGORITHMS
2 Decision Trees
2.1 Introduction2.2 Entropy2.2.1 Example2.2.2 Understanding the Concept of Number of Bits2.3 Attribute Selection Measure2.3.1 Information Gain of ID32.3.2 The Problem with Information Gain2.4 Implementation in MATLAB®2.4.1 Gain Ratio of C4.52.4.2 Implementation in MATLABReferences
3 Rule-Based Classifiers
3.1 Introduction to Rule-Based Classifiers3.2 Sequential Covering Algorithm3.3 Algorithm3.4 Visualization3.5 Ripper3.5.1 Algorithm3.5.2 Understanding Rule Growing Process3.5.3 Information Gain3.5.4 Pruning3.5.5 OptimizationReferences
4 Naïve Bayesian Classification
4.1 Introduction4.2 Example4.3 Prior Probability4.4 Likelihood4.5 Laplace Estimator4.6 Posterior Probability4.7 MATLAB ImplementationReferences
5 The k-Nearest Neighbors Classifiers
5.1 Introduction5.2 Example5.3 k-Nearest Neighbors in MATLAB®References
6 Neural Networks
6.1 Perceptron Neural Network6.1.1 Perceptrons6.2 MATLAB Implementation of the Perceptron Training and Testing Algorithms6.3 Multilayer Perceptron Networks6.4 The Backpropagation Algorithm6.4.1 Weights Updates in Neural Networks6.5 Neural Networks in MATLABReferences
7 Linear Discriminant Analysis
7.1 Introduction7.2 ExampleReferences
8 Support Vector Machine
8.1 Introduction8.2 Definition of the Problem8.2.1 Design of the SVM8.2.2 The Case of Nonlinear Kernel8.3 The SVM in MATLAB®References
SECTION II UNSUPERVISED LEARNING ALGORITHMS
9 k-Means Clustering
9.1 Introduction9.2 Description of the Method9.3 The k-Means Clustering Algorithm9.4 The k-Means Clustering in MATLAB®
10 Gaussian Mixture Model
10.1 Introduction10.2 Learning the Concept by ExampleReferences
11 Hidden Markov Model
11.1 Introduction11.2 Example11.3 MATLAB CodeReferences
12 Principal Component Analysis
12.1 Introduction12.2 Description of the Problem12.3 The Idea behind the PCA12.3.1 The SVD and Dimensionality Reduction12.4 PCA Implementation12.4.1 Number of Principal Components to Choose12.4.2 Data Reconstruction Error12.5 The Following MATLAB® Code Applies the PCA12.6 Principal Component Methods in Weka12.7 Example: Polymorphic Worms Detection Using PCA12.7.1 Introduction12.7.2 SEA, MKMP, and PCA12.7.3 Overview and Motivation for Using String Matching12.7.4 The KMP Algorithm12.7.5 Proposed SEA12.7.6 An MKMP Algorithm12.7.6.1 Testing the Quality of the Generated Signature for Polymorphic Worm A12.7.7 A Modified Principal Component Analysis12.7.7.1 Our Contributions in the PCA12.7.7.2 Testing the Quality of Generated Signature for Polymorphic Worm A12.7.7.3 Clustering Method for Different Types of Polymorphic Worms12.7.8 Signature Generation Algorithms Pseudo-Codes12.7.8.1 Signature Generation ProcessReferences
Appendix I: Transcript of Conversations with Chatbot
Appendix II: Creative Chatbot
Index

Content preview from Machine Learning

Chapter 10 Gaussian Mixture Model

10.1 Introduction

As we know that each Gaussian is represented by a combination of mean and variance, if we have a mixture of M Gaussian distributions, then the weight of each Gaussian will be a third parameter related to each Gaussian distribution in a Gaussian mixture model (GMM). The following equation represents a GMM with M components.

$p (x | θ) = \sum_{k = 1}^{M} w_{k} p (x | θ_{k})$

where w_k represents the weight of the kth component. The mean and covariance of kth components are represented by θ_k = (µ_k, ∑_k). p(x|θ_k), which is the Gaussian density of the kth component and is a D-variate Gaussian function of the following form:

$p (x | θ_{k}) or p (x | (μ_{k}, \sum_{k})) = \frac{1}{2 π^{D / 2} {| \sum_{k} |}^{1 / 2}} e^{{- (1 / 2) (x - μ_{k})^{'} \sum_{k}^{- 1} (x - μ_{k})}}$

Sum of values of w_k for different values ...

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ISBN: 9781315354415

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Machine Learning

by Mohssen Mohammed, Muhammad Badruddin Khan, Eihab Mohammed Bashier

Chapter 10

Gaussian Mixture Model

10.1 Introduction

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