skip to content
Autonomous learning systems : from data streams to knowledge in real-time Preview this item
ClosePreview this item

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



(not yet rated) 0 with reviews - Be the first.

More like this


Find a copy in the library

&AllPage.SpinnerRetrieving; Finding libraries that hold this item...


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.


Editorial reviews

Publisher Synopsis


User-contributed reviews
Retrieving GoodReads reviews...
Retrieving DOGObooks reviews...


Be the first.

Similar Items

Confirm this request

You may have already requested this item. Please select Ok if you would like to proceed with this request anyway.

Linked Data

schema:name"Autonomous learning systems : from data streams to knowledge in real-time"@en

Content-negotiable representations

Close Window

Please sign in to WorldCat 

Don't have an account? You can easily create a free account.