SECTION II UNSUPERVISED LEARNING ALGORITHMS
by Eihab Mohammed Bashier, Muhammad Badruddin Khan, Mohssen Mohammed
Machine Learning
- Cover
- Half Title
- Title Page
- Copyright Page
- Table of Contents
- Dedication
- Preface
- Acknowledgments
- Authors
- Introduction
- 1 Introduction to Machine Learning
- SECTION I SUPERVISED LEARNING ALGORITHMS
- 3 Rule-Based Classifiers
- 4 Naïve Bayesian Classification
- 5 The k-Nearest Neighbors Classifiers
- 6 Neural Networks
- 7 Linear Discriminant Analysis
- 8 Support Vector Machine
- SECTION II UNSUPERVISED LEARNING ALGORITHMS
- 9 k-Means Clustering
- 10 Gaussian Mixture Model
- 11 Hidden Markov Model
- 12 Principal Component Analysis
- 12.1 Introduction
- 12.2 Description of the Problem
- 12.3 The Idea behind the PCA
- 12.4 PCA Implementation
- 12.5 The Following MATLAB® Code Applies the PCA
- 12.6 Principal Component Methods in Weka
- 12.7 Example: Polymorphic Worms Detection Using PCA
- References
- Appendix I: Transcript of Conversations with Chatbot
- Appendix II: Creative Chatbot
- Index
II
UNSUPERVISED LEARNING ALGORITHMS
Introduction
In Section II, we will discuss the following algorithms:
K-means
Gaussian mixture model
Hidden Markov model
Principal component analysis in context of dimensionality reduction
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