作者：张军英
页数：200
出版社：西安电子科技大学出版社
出版日期：2021
ISBN：9787560660547
电子书格式：pdf/epub/txt
内容简介
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.
本书特色
系统阐述机器学习的思想、技术与方法 注重回答为什么学、从哪里学、学什么、怎么学、学好了吗以及学习意味着什么等机器学习的核心基础问题 全面培养学生的人工智能和大数据处理能力
目录
1.1 ABOUT LEARNING 1
1.2 LEARN FROM WHERE: DATA 2
1.3 WHAT TO GET FROM LEARNING: PATTERNS 3
1.4 HOW TO LEARN: SCHEMES 5
1.5 HOW TO EVALUATE: GENERALIZATION 9
1.6 LEARN FOR WHAT: ENGINEERINGS AND/OR SCIENCES 10
1.7 LEARN TO BE INTELLIGENT 14
1.8 SUMMARY 15
REFERENCES 16
CHAPTER 2 PERFORMANCE EVALUATION 17
2.1 EVALUATING A MODEL 17
2.2 COMPARISON TEST 22
2.3 BIAS睼ARIANCE DECOMPOSITION AND SYSTEM DEBUGGING 24
2.4 CLUSTER VALIDITY INDICES 32
2.5 SUMMARY 33
REFERENCES 33
CHAPTER 3 REGRESSION ANALYSIS 35
3.1 REGRESSION PROBLEM 35
3.2 LINEAR REGRESSION 36
3.3 LOGISTIC REGRESSION 40
3.4 REGULARIZATION 43
3.5 SUMMARY 48
REFERENCES 49
CHAPTER 4 PERCEPTRON AND MULTILAYER PERCEPTRON 50
4.1 PERCEPTRON 50
4.2 MULTILAYER PERCEPTRON 59
4.3 MLP IN APPLICATIONS 66
4.4 SUMMARY 67
REFERENCES 68
CHAPTER 5 SUPPORT VECTOR MACHINES 70
5.1 LINEAR SUPPORT VECTOR MACHINE 70
5.2 NONLINEAR SUPPORT VECTOR MACHINE 75
5.3 SUPPORT VECTOR REGRESSION 76
5.4 MERITS AND LIMITATIONS 78
5.5 SUMMARY 80
REFERENCES 80
CHAPTER 6 UNSUPERVISED LEARNING 83
6.1 THE TASK OF CLUSTERING 83
6.2 SIMILARITY MEASURES 84
6.3 K睲EANS 91
6.4 SELF睴RGANIZING MAP 94
6.5 SUMMARY 100
REFERENCES 100
Chapter7 REPRESENTATION LEARNING 103
7.1 PRINCIPAL COMPONENTS ANALYSIS(PCA) 104
7.2 LINEAR DISCRIMINANT ANALYSIS (LDA) 110
7.3 INDEPENDENT COMPONENT ANALYSIS (ICA) 113
7.4 NON睳EGATIVE MATRIX FACTORIZATION (NMF) 119
7.5 SUMMARY 122
REFERENCES 123
CHAPTER 8 PROBLEM DECOMPOSITION 126
8.1 CODING AND DECODING 126
8.2 DISTRIBUTED OUTPUT CODE 129
8.3 ERROR睠ORRECTING OUTPUT CODE 130
8.4 SUMMARY 135
REFERENCES 136
CHAPTER 9 ENSEMBLE LEARNING 138
9.1 DESIGN OF A MULTIPLE CLASSIFIER SYSTEM 138
9.2 DESIGN OF CLASSIFIER ENSEMBLES 139
9.3 DESIGN OF COMBINATION RULES 142
9.4 AN MCS INSTANCE: PSO瞁CM 144
9.5 SUMMARY 148
REFERENCES 149
CHAPTER 10 CONVOLUTIONAL NEURAL NETWORK 151
10.1 WHY NOT A DEEP MLP 151
10.2 CONVOLUTION OPERATION 153
10.3 CONVOLUTIONAL NEURAL NETWORK 156
10.4 HYPER PARMAETERS 163
10.5 AN EXAMPLE 165
10.6 SUMMARY 167
REFERENCES 168
CHAPTER 11 ARTIFICIAL INTELLIGENCE AIDED MENINGITIS DIAGNOSTIC SYSTEM
170
11.1 DATA SET AND PRE睵ROCESSING 170
11.2 LEARNING A DIAGNOSTIC MODEL 172
11.3 PERFORMANCE EVALUATION 174
REFERENCES 179
CHAPTER12 CHALLENGES AND OPPORTUNITIES 181
12.1 TODAY’S MACHINE LEARNING 181
12.2 CHALLENGES AND OPPORTUNITIES 183
REFERENCES 189
