Machine learning in cognitive IoT|RISS 상세보기

목차 (Table of Contents)

CONTENTS
Preface = xiii
Acknowledgements = xv
List of Figures = xvii
List of Tables = xxi

CONTENTS
Preface = xiii
Acknowledgements = xv
List of Figures = xvii
List of Tables = xxi
1 Internet of Things = 1
1.1 IoT History = 3
1.2 IoT Architecture = 4
1.3 IoT Elements = 5
1.3.1 Wireless Sensor Networks (WSN) = 5
1.3.2 Radio Frequency Identification (RFID) = 6
1.3.3 Datastorage = 8
1.3.4 Challenging Issues = 9
1.4 Data Analytics = 9
1.4.1 IoT Data Sources = 9
1.4.2 Data Processing = 10
1.4.3 IoT Technologies = 11
1.4.4 Optimization Techniques = 12
1.5 Steps of Data Preprocessing = 13
1.5.1 Data Formatting = 13
1.5.2 Data Cleaning = 13
1.5.3 Data Reduction Schemes = 14
1.6 IoT Protocols = 16
1.6.1 Infrastructure Layer (Network／Transport Layer) = 16
1.6.2 Data Protocols = 17
1.6.3 Physical Layer = 17
1.6.4 LPWAN : Low Power Wide Area Network = 18
1.7 IoT Applications = 18
1.7.1 Logistics and Transportation = 19
1.7.2 Home and Workplace = 19
1.7.3 Personal and Social = 19
1.7.4 Health Domain = 20
1.8 Book Outline = 20
1.9 Target Audience = 22
1.10 Summary and What's Next? = 22
1.11 Exercises = 23
2 Cognitive Internet of Things = 27
2.1 Cognitive Devices = 27
2.2 Cognitive in IoT = 28
2.3 CIoT Background = 29
2.4 CIoT Elements = 31
2.4.1 Sensors = 31
2.4.2 Machine Learning = 31
2.4.3 Cloud Storage = 32
2.5 How Do Cognitive Devices Act as Human Assistants? = 32
2.6 Machine-to-machine Interfaces = 34
2.6.1 Language = 34
2.6.2 Interpersonal Relationship = 35
2.7 Man-to-machine Communication = 36
2.8 Machine-to-web Communication(M2W) = 36
2.9 CIoT Applications = 37
2.9.1 Cognitive Living = 38
2.9.2 Cognitive Cities = 39
2.9.3 Cognitive Health = 39
2.9.4 Auto-casting and Auto-reacting Cognition Systems = 40
2.10 Summary and What's Next? = 41
2.11 Exercises = 41
3 Data Mining in IoT = 47
3.1 Search Engines as a Medium = 47
3.2 Data Creation and Retrieval Scheme = 48
3.3 Data Mining = 50
3.3.1 Data Mining Functions = 51
3.3.2 Relation of Data Science with Machine Learning = 56
3.4 Data Mining in IoT = 57
3.5 Machine Learning in IoT = 58
3.6 Summary and What's Next? = 60
3.7 Exercises = 61
4 Machine Learning Techniques = 67
4.1 Tools to Implement Machine Learning = 69
4.1.1 Python = 70
4.1.2 R = 70
4.1.3 Matlab = 71
4.1.4 Weka = 71
4.2 Experiments = 71
4.2.1 Dataset = 75
4.3 Supervised Learning = 75
4.3.1 Unsupervised Learning = 76
4.4 Classification = 77
4.5 Regression = 78
4.6 Clustering = 79
4.7 Summary and What's Next? = 79
4.8 Exercises = 81
5 R Programming = 87
5.1 Introduction = 87
5.1.1 Basis of the R programming = 88
5.1.2 Installing R = 89
5.1.3 Working in R = 89
5.2 Basic Commands = 89
5.2.1 Assignment = 89
5.2.2 Comments = 91
5.3 Data Types = 91
5.3.1 Basic Data Types = 92
5.3.2 Structural Data Type = 92
5.3.3 Operators = 99
5.3.4 Graphics = 101
5.3.5 Basic Statistics = 105
5.3.6 Packages = 110
5.3.7 Input Parameters Formats for R = 111
5.4 Summary and What's Next? = 116
5.5 Exercises = 117
6 Machine Learning Paradigms = 123
6.1 Introduction = 123
6.2 Generalizing Input = 125
6.3 Generalizing Output = 127
6.3.1 Decision Tree = 127
6.4 Classification Rules = 129
6.5 Numeric Prediction = 131
6.6 Instance-based Learning = 135
6.6.1 Distance Metric = 137
6.7 Summary and What's Next = 138
6.8 Exercises = 139
7 Different Machine Learning Models = 145
7.1 Linear Method for Regression = 145
7.2 Linear Method for Classification = 146
7.3 Kernel Smoothing Models = 149
7.4 Back Propagation = 150
7.4.1 Radial Basis Function Networks = 152
7.5 Neural Network = 152
7.5.1 The Perceptron = 153
7.6 Bayesian Methods = 154
7.6.1 Bayesian Statistics = 154
7.6.2 Bayesian Inference = 155
7.7 Summary and What's Next = 156
7.8 Exercises = 157
8 Data Processing = 161
8.1 Input Preparation = 161
8.2 Data Preprocessing = 165
8.3 Data Cleaning = 166
8.3.1 The Condensed Nearest Neighbor Rule = 166
8.3.2 Tomek = 168
8.3.3 One-sided Selection = 170
8.3.4 SMOTE = 172
8.3.5 ADASYN Algorithm = 174
8.3.6 SOTU = 177
8.4 Summary and What's Next? = 179
8.5 Exercises = 180
9 Feature Engineering and Optimization = 183
9.1 Feature Reduction = 184
9.1.1 Principal Component Analysis = 184
9.2 Feature Selection = 194
9.2.1 Feature Importance = 196
9.2.2 Recursive Feature Elimination = 209
9.3 Machine Learning Models = 211
9.3.1 Experiments = 214
9.4 Bagging and Boosting Techniques = 214
9.4.1 Bagging = 214
9.4.2 Boosting = 219
9.5 Ensemble Approach = 220
9.6 Summary and What's Next = 221
9.7 Exercises = 222
10 Evaluation and Validation of Results = 227
10.1 Confusion Matrix = 228
10.2 Correlation = 229
10.2.1 Covariance = 230
10.2.2 Pearson's Correlation = 230
10.2.3 Spearman's Correlation = 231
10.2.4 Matthews' Correlation Coefficient (MCC) = 233
10.3 Coefficient of Determinant : R2 = 234
10.4 Accuracy (ACC) = 234
10.5 ROC and AUC = 235
10.6 Error in Regression = 235
10.6.1 Root Mean Squared Error (RMSE) = 235
10.6.2 Mean Absolute Error (MAE) = 236
10.6.3 Relative Squared Error (RSE) = 236
10.6.4 Relative Absolute Error (RAE) = 237
10.7 Measuring Rates = 237
10.7.1 Sensitivity, Recall, Hit Rate, or True Positive Rate (TPR) = 237
10.7.2 Specificity, Selectivity, or True Negative Rate (TNR) = 237
10.7.3 Precision, Positive Predictive Value (PPV) = 237
10.7.4 Recall, Sensitivity, Hit Rate, or True Positive Rate (TPR) = 237
10.7.5 Fallout, False Positive Rate (FPR) = 237
10.7.6 Miss Rate or False Negative Rate (FNR) = 237
10.7.7 False Discovery Rate (FDR) = 238
10.7.8 False Omission Rate (FOR) = 238
10.8 F Measure = 238
10.9 Summary and What's Next? = 238
10.10 Exercises = 239
11 Solutions = 245
11.1 Chapter 1 = 245
11.2 Chapter 2 = 245
11.3 Chapter 3 = 246
11.4 Chapter 4 = 247
11.5 Chapter 5 = 247
11.6 Chapter 6 = 248
11.7 Chapter 7 = 248
11.8 Chapter 8 = 249
11.9 Chapter 9 = 249
11.10 Chapter 10 = 250
12 Dataset = 251
Bibliography = 289
Index = 293

서점명	서명	판매현황	종이책			전자책 구매링크
서점명	서명	판매현황	정가	판매가(할인율)	포인트(포인트몰)	전자책 구매링크
	Machine Learning in Cognitive IoT	판매중	235,720원	212,140원 (10%)	10,610포인트 (5%)

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