This book provides a comprehensive foundation of machine learning. To answer the questions of what to learn, how to learn, what to get from learning, and how to evaluate, as well as what is meant by learning, the book focuses on the fundamental basics of machine learning, its methodology, theory, algorithms, and evaluations, together with some philosophical thinking on comparison between machine learning and human learning for machinery intelligence.
The book is organized as follows: Introduction (Chapter 1), Evaluation (Chapter 2), Supervised learning (Chapters 3, 4, and 5), Unsupervised learning (Chapter 6), Representation learning (Chapter 7), Problem decomposition (Chapter 8), Ensemble learning (Chapter 9), Deep learning (Chapter 10), Application (Chapter 11), and Challenges (Chapter 12).
The book can be used as a textbook for college, undergraduate, graduate and PhD students majored in computer science, automation, electronic engineering, communication, ect. It can also be used as a reference for readers who are interested in machine learning and hope to make contributions to the field.
Table of Contents
CHAPTER 1 INTRODUCTION
1．1 ABOUT LEARNING
1．2 LEARN FROM WHERE： DATA
1．3 WHAT TO GET FROM LEARNING： PATTERNS
1．4 HOW TO LEARN： SCHEMES
1．5 HOW TO EVALUATE： GENERALIZATION
1．6 LEARN FOR WHAT： ENGINEERINGS AND/OR SCIENCES
1．7 LEARN TO BE INTELLIGENT
1．8 SUMMARY
REFERENCES
CHAPTER 2 PERFORMANCEEVALUATION
2．1 EVALUATING A MODEL
2．2 COMPARISON TEST
2．3 BIAS-VARIANCE DECOMPOSITION AND SYSTEM DEBUGGING
2．4 CLUSTER VALIDITY INDICES
2．5 SUMMARY
REFERENCES
CHAPTER 3 REGRESSION ANALYSIS
3．1 REGRESSION PROBLEM
3．2 LINEAR REGRESSION
3．3 LOGISTIC REGRESSION
3．4 REGULARIZATION
3．5 SUMMARY
REFERENCES
CHAPTER 4 PERCEPTRON AND MULTILAYER PERCEPTRON
4．1 PERCEPTRON
4．2 MULTILAYER PERCEPTRON
4．3 MLP IN APPLICATIONS
4．4 SUMMARY
REFERENCES
CHAPTER 5 SUPPORT VECTOR MACHINES
5．1 LINEAR SUPPORT VECTOR MACHINE
5．2 NONLINEAR SUPPORT VECTOR MACHINE
5．3 SUPPORT VECTOR REGRESSION
5．4 MERITS AND LIMITATIONS
5．5 SUMMARY
REFERENCES
CHAPTER 6 UNSUPERVISED LEARNING
6．1 THE TASK OF CLUSTERING
6．2 SIMILARITY MEASURES
6．3 K-MEANS
6．4 SELF-ORGANIZING MAP
6．5 SUMMARY
REFERENCES
CHAPTER 8 PROBLEM DECOMPOSITION
8．1 CODING AND DECODING
8．2 DISTRIBUTED OUTPUT CODE
8．3 ERROR-CORRECTING OUTPUT CODE
8．4 SUMMARY
REFERENCES
CHAPTER 9 ENSEMBLE LEARNING
9．1 DESIGN OF A MULTIPLE CLASSIFIER SYSTEM
9．2 DESIGN OF CLASSIFIER ENSEMBLES
9．3 DESIGN OF COMBINATION RULES
9．4 AN MCS INSTANCE： PSO-WCM
9．5 SUMMARY
REFERENCES
……
CHAPTER 10 CONVOLUTIONAL NEURAL NETWORK
CHAPTER 11 ARTIFICIAL INTELLIGENCE AIDED MENINGITIS DIAGNOSTIC SYSTEM
CHAPTER 12 CHALLENGES AND OPPORTUNITIES
