Ripple-Down Rules - The Alternative to Machine Learning(2021)[Kang][9781003126157]

Description

Cover Half Title Title Page Copyright Page Table of Contents Preface Acknowledgements About the Authors Chapter 1: Problems with Machine Learning and Knowledge Acquisition 1.1 Introduction 1.2 Machine Learning 1.3 Knowledge Acquisition Note Chapter 2: Philosophical Issues in Knowledge Acquisition 1 Notes Chapter 3: Ripple-Down Rule Overview 3.1 Case-driven Knowledge Acquisition 3.2 Order of Cases Processed 3.3 Linked Production Rules 3.4 Adding Rules 3.5 Assertions and Retractions 3.6 Formulae in Conclusions Note Chapter 4: Introduction to Excel_RDR Note Chapter 5: Single Classification Example 5.1 Repetition in an SCRDR Knowledge Base 5.2 SCRDR Evaluation and Machine Learning Comparison 5.3 Summary Notes Chapter 6: Multiple Classification Example 6.1 Introduction to Multiple Classification Ripple-Down Rules (MCRDR) 6.2 Excel_MCRDR Example 6.3 Discussion: MCRDR for Single Classification 6.4 Actual Multiple Classification with MCRDR 6.5 Discussion 6.6 Summary Chapter 7: General Ripple-Down Rules (GRDR) 7.1 Background 7.2 Key Features of GRDR 7.2.1 Linked Production Rules 7.2.2 No Retraction 7.2.3 Inference Restarts After a Fact Is Asserted 7.2.4 GRDR Knowledge Base Structure 7.2.5 The Final Conclusion 7.3 Excel_GRDR Demo 7.4 Discussion: GRDR, MCRDR and SCRDR Note Chapter 8: Implementation and Deployment of an RDR-Based System 8.1 Validation 8.2 The Role of the User/Expert 8.3 Cornerstone Cases 8.4 Explanation 8.5 Implementation Issues 8.6 Information System Interfaces Notes Chapter 9: RDR and Machine Learning 9.1 Suitable Datasets 9.2 Human Experience Versus Statistics 9.3 Unbalanced Data 9.4 Prudence 9.5 RDR-based Machine Learning Methods 9.6 Machine Learning Combined with RDR Knowledge Acquisition 9.7 Machine Learning Supporting RDR 9.8 Summary Note Appendix 1: Industrial Applications of RDR A1.1 PEIRS (1991–1995) A1.2 Pacific Knowledge Systems A1.3 Ivis A1.4 Erudine Pty Ltd A1.5 Ripple-Down Rules at IBM A1.6 YAWL A1.7 Medscope A1.8 Seegene A1.9 IPMS A1.10 Tapacross A1.11 OTHER Notes Appendix 2: Research-Demonstrated Applications A2.1 RDR Wrappers A2.1.1 SAP Duplicate Invoices A2.1.2 RDR Wrapper for the Stanford Named Entity Recogniser A2.1.3 RDR Wrapper for REVERB A2.1.4 Machine Learning and RDR Wrappers A2.2 Text-processing, Natural Language Processing and Information Retrieval A2.2.1 Relationship Extraction A2.2.2 Temporal Relations A2.2.3 Positive Attributions A2.2.4 Part of Speech Tagging A2.2.5 Named Entity Recognition A2.2.6 Extracting Processes A2.2.7 Email Classification A2.2.8 Web Monitoring A2.2.9 Other Language Tasks A2.3 Conversational Agents and Help Desks A2.3.1 Conversational Agents A2.3.2 Help Desks A2.4 RDR for Operator and Parameter Selection A2.4.1 Genetic Algorithms A2.4.2 Image Processing A2.4.3 Configuring and Selecting Web Resources A2.4.4 Autopilots A2.4.5 Multiple Agent Games A2.4.6 Configuration A2.4.7 Energy Efficiency A2.5 Anomaly and Event Detection A2.5.1 Network Intrusion and Bank Fraud A2.5.2 Stock Market Events A2.6 RDR for Image and Video Processing A2.6.1 RDR and Machine Learning for Image Processing A2.6.2 RDR for Image Processing Parameters A2.6.3 RDR for Combining Image Primitives A2.6.4 Video Processing References Index A B C D E F G H I J K L M N O P Q R S T U V W X Z