机器学习的综合基础(Machine Learning——A Comprehensive Foundation)

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2星价￥29.6 定价￥37.0

作者：张军英

出版社：西安电子科技大学出版社

本类榜单：教材

分类：教材 > 研究生/本科/专科教材 > 理学

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ISBN：9787560660547
装帧：一般胶版纸
册数：暂无
重量：暂无
开本：其他
页数：200
出版时间：2021-08-01
条形码：9787560660547 ; 978-7-5606-6054-7

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

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.

CHAPTER 1 INTRODUCTION 1 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VARIANCE 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MEANS 91 6.4 SELFORGANIZING 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NEGATIVE 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CORRECTING 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WCM 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PROCESSING 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

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