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Autonomous learning systems : from data streams to knowledge in real-time

Author: Plamen P Angelov
Publisher: Chichester, West Sussex, United Kingdom : Wiley, a John Wiley & Sons, Ltd., Publication, 2013.
Edition/Format:   Print book : EnglishView all editions and formats
Database:WorldCat
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Document Type: Book
All Authors / Contributors: Plamen P Angelov
ISBN: 9781119951520 1119951526 1118481909 9781118481905
OCLC Number: 798615034
Description: xxiv, 273 pages : illustrations ; 26 cm
Contents: <p>Forewords xi <p>Preface xix <p>About the Author xxiii <p>1 Introduction 1 <p>1.1 Autonomous Systems 3 <p>1.2 The Role of Machine Learning in Autonomous Systems 4 <p>1.3 System Identification an Abstract Model of the RealWorld 6 <p>1.4 Online versus Offline Identification 9 <p>1.5 Adaptive and Evolving Systems 10 <p>1.6 Evolving or Evolutionary Systems 11 <p>1.7 Supervised versus Unsupervised Learning 13 <p>1.8 Structure of the Book 14 <p>PART I FUNDAMENTALS <p>2 Fundamentals of Probability Theory 19 <p>2.1 Randomness and Determinism 20 <p>2.2 Frequentistic versus Belief-Based Approach 22 <p>2.3 Probability Densities and Moments 23 <p>2.4 Density Estimation Kernel-Based Approach 26 <p>2.5 Recursive Density Estimation (RDE) 28 <p>2.6 Detecting Novelties/Anomalies/Outliers using RDE 32 <p>2.7 Conclusions 36 <p>3 Fundamentals of Machine Learning and Pattern Recognition37 <p>3.1 Preprocessing 37 <p>3.2 Clustering 42 <p>3.3 Classification 56 <p>3.4 Conclusions 58 <p>4 Fundamentals of Fuzzy Systems Theory 61 <p>4.1 Fuzzy Sets 61 <p>4.2 Fuzzy Systems, Fuzzy Rules 64 <p>4.3 Fuzzy Systems with Nonparametric Antecedents (AnYa) 69 <p>4.4 FRB (Offline) Classifiers 73 <p>4.5 Neurofuzzy Systems 75 <p>4.6 State Space Perspective 79 <p>4.7 Conclusions 81 <p>PART II METHODOLOGY OF AUTONOMOUS LEARNING SYSTEMS <p>5 Evolving System Structure from Streaming Data 85 <p>5.1 Defining System Structure Based on Prior Knowledge 85 <p>5.2 Data Space Partitioning 86 <p>5.3 Normalisation and Standardisation of Streaming Data in anEvolving Environment 96 <p>5.4 Autonomous Monitoring of the Structure Quality 98 <p>5.5 Short- and Long-Term Focal Points and Submodels 104 <p>5.6 Simplification and Interpretability Issues 105 <p>5.7 Conclusions 107 <p>6 Autonomous Learning Parameters of the Local Submodels109 <p>6.1 Learning Parameters of Local Submodels 110 <p>6.2 Global versus Local Learning 111 <p>6.3 Evolving Systems Structure Recursively 113 <p>6.4 Learning Modes 116 <p>6.5 Robustness to Outliers in Autonomous Learning 118 <p>6.6 Conclusions 118 <p>7 Autonomous Predictors, Estimators, Filters, InferentialSensors 121 <p>7.1 Predictors, Estimators, Filters Problem Formulation121 <p>7.2 Nonlinear Regression 123 <p>7.3 Time Series 124 <p>7.4 Autonomous Learning Sensors 125 <p>7.5 Conclusions 131 <p>8 Autonomous Learning Classifiers 133 <p>8.1 Classifying Data Streams 133 <p>8.2 Why Adapt the Classifier Structure? 134 <p>8.3 Architecture of Autonomous Classifiers of the FamilyAutoClassify 135 <p>8.4 Learning AutoClassify from Streaming Data 139 <p>8.5 Analysis of AutoClassify 140 <p>8.6 Conclusions 140 <p>9 Autonomous Learning Controllers 143 <p>9.1 Indirect Adaptive Control Scheme 144 <p>9.2 Evolving Inverse Plant Model from Online Streaming Data145 <p>9.3 Evolving Fuzzy Controller Structure from Online StreamingData 147 <p>9.4 Examples of Using AutoControl 148 <p>9.5 Conclusions 153 <p>10 Collaborative Autonomous Learning Systems 155 <p>10.1 Distributed Intelligence Scenarios 155 <p>10.2 Autonomous Collaborative Learning 157 <p>10.3 Collaborative Autonomous Clustering, AutoCluster bya Team of ALSs 158 <p>10.4 Collaborative Autonomous Predictors, Estimators, Filtersand AutoSense by a Team of ALSs 159 <p>10.5 Collaborative Autonomous Classifiers AutoClassify bya Team of ALSs 160 <p>10.6 Superposition of Local Submodels 161 <p>10.7 Conclusions 161 <p>PART III APPLICATIONS OF ALS <p>11 Autonomous Learning Sensors for Chemical and PetrochemicalIndustries 165 <p>11.1 Case Study 1: Quality of the Products in an Oil Refinery165 <p>11.2 Case Study 2: Polypropylene Manufacturing 172 <p>11.3 Conclusions 178 <p>12 Autonomous Learning Systems in Mobile Robotics 179 <p>12.1 The Mobile Robot Pioneer 3DX 179 <p>12.2 Autonomous Classifier for Landmark Recognition 180 <p>12.3 Autonomous Leader Follower 193 <p>12.4 Results Analysis 196 <p>13 Autonomous Novelty Detection and Object Tracking in VideoStreams 197 <p>13.1 Problem Definition 197 <p>13.2 Background Subtraction and KDE for Detecting VisualNovelties 198 <p>13.3 Detecting Visual Novelties with the RDE Method 203 <p>13.4 Object Identification in Image Frames Using RDE 204 <p>13.5 Real-time Tracking in Video Streams Using ALS 206 <p>13.6 Conclusions 209 <p>14 Modelling Evolving User Behaviour with ALS 211 <p>14.1 User Behaviour as an Evolving Phenomenon 211 <p>14.2 Designing the User Behaviour Profile 212 <p>14.3 Applying AutoClassify0 for Modelling Evolving UserBehaviour 215 <p>14.4 Case Studies 216 <p>14.5 Conclusions 221 <p>15 Epilogue 223 <p>15.1 Conclusions 223 <p>15.2 Open Problems 227 <p>15.3 Future Directions 227 <p>APPENDICES <p>Appendix A Mathematical Foundations 231 <p>Appendix B Pseudocode of the Basic Algorithms 235 <p>References 245 <p>Glossary 259 <p>Index 263
Responsibility: Plamen Angelov, Lancaster University, UK.

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