Book description
Machine Learning Proceedings 1989
Table of contents
- Front Cover
- Proceedings of the Sixth International Workshop on Machine Learning
- Copyright Page
- Table of Contents (1/2)
- Table of Contents (2/2)
- PREFACE
-
Part 1: Combining Empirical and Explanation-Based Learning
- Chapter 1. Unifying Themes in Empirical and Explanation-Based Learning
- CHAPTER 2. INDUCTION OVER THE UNEXPLAINED: Integrated Learning of Concepts with Both Explainable and Conventional Aspects
- CHAPTER 3. CONCEPTUAL CLUSTERING OF EXPLANATIONS
- Chapter 4. A Tight Integration of Deductive and Inductive Learning
- CHAPTER 5. MULTI-STRATEGY LEARNING IN NONHOMOGENEOUS DOMAIN THEORIES
- CHAPTER 6. A DESCRIPTION OF PREFERENCE CRITERION IN CONSTRUCTIVE LEARNING: A Discussion of Basic Issues
- CHAPTER 7. COMBINING CASE-BASED REASONING, EXPLANATION-BASED LEARNING, AND LEARNING FROM INSTRUCTION
- CHAPTER 8. DEDUCTION IN TOP-DOWN INDUCTIVE LEARNING
- CHAPTER 9. ONE-SIDED ALGORITHMS FOR INTEGRATING EMPIRICAL AND EXPLANATION-BASED LEARNING
- CHAPTER 10. COMBINING EMPIRICAL AND ANALYTICAL LEARNING WITH VERSION SPACES
- CHAPTER 11. FINDING NEW RULES FOR INCOMPLETE THEORIES: EXPLICIT BIASES FOR INDUCTION WITH CONTEXTUAL INFORMATION
- CHAPTER 12. LEARNING FROM PLAUSIBLE EXPLANATIONS
- CHAPTER 13. AUGMENTING DOMAIN THEORY FOR EXPLANATION-BASED GENERALISATION
- Chapter 14. Explanation Based Learning as Constrained Search
- CHAPTER 15. REDUCING SEARCH AND LEARNING GOAL PREFERENCES
- Chapter 16. Adaptation-Based Explanation: Explanations as Cases
- CHAPTER 17. A RETRIEVAL MODEL USING FEATURE SELECTION
- CHAPTER 18. IMPROVING DECISION-MAKING ON THE BASIS OF EXPERIENCE
- CHAPTER 19. EXPLANATION-BASED ACCELERATION OF SIMILARITY-BASED LEARNING
- Chapter 20. Knowledge Acquisition Planning: Results and Prospects
- Chapter 21. "Learning by instruction" in connectionist systems
- CHAPTER 22. INTEGRATING LEARNING IN A NEURAL NETWORK
- Chapter 23. Explanation-based learning with weak domain theories
- Chapter 24. Using Domain Knowledge to Improve Inductive Learning Algorithms for Diagnosis
- Chapter 25. A Framework for Improving Efficiency and Accuracy
- CHAPTER 26. ERROR CORRECTION IN CONSTRUCTIVE INDUCTION
- CHAPTER 27. IMPROVING EXPLANATION-BASED INDEXING WITH EMPIRICAL LEARNING
- CHAPTER 28. A SCHEMA FOR AN INTEGRATED LEARNING SYSTEM
- CHAPTER 29. COMBINING EXPLANATION-BASED LEARNING AND ARTIFICIAL NEURAL NETWORKS
-
Part 2: Empirical Learning; Theory and Application
- CHAPTER 30. LEARNING CLASSIFICATION RULES USING BAYES
- CHAPTER 31. NEW EMPIRICAL LEARNING MECHANISMS PERFORM SIGNIFICANTLY BETTER IN REAL LIFE DOMAINS
- CHAPTER 32. INDUCTIVE LEARNING WITH BCT
- CHAPTER 33. WHAT GOOD ARE EXPERIMENTS?
- Chapter 34. An Experimental Comparison of Human and Machine Learning Formalisms
- CHAPTER 35. TWO ALGORITHMS THAT LEARN DNF BY DISCOVERING RELEVANT FEATURES
- CHAPTER 36. LIMITATIONS ON INDUCTIVE LEARNING
- CHAPTER 37. THE INDUCTION OF PROBABILISTIC RULE SETS — THE ITRULE ALGORITHM
- CHAPTER 38. EMPIRICAL SUBSTRUCTURE DISCOVERY
- CHAPTER 39. LEARNING THE BEHAVIOR OF DYNAMICAL SYSTEMS FROM EXAMPLES
- CHAPTER 40. EXPERIMENTS IN ROBOT LEARNING
- Chapter 41. Induction of Decision Trees from Inconclusive Data
- CHAPTER 42. KNOWLEDGE INTENSIVE INDUCTION
- CHAPTER 43. AN OUNCE OF KNOWLEDGE IS WORTH A TON OF DATA: Quantitative Studies of the Trade-Off between Expertise and Data based on Statistically Weil-Founded Empirical Induction
- CHAPTER 44. SIGNAL DETECTION THEORY: VALUABLE TOOLS FOR EVALUATING INDUCTIVE LEARNING
- CHAPTER 45. UNKNOWN ATTRIBUTE VALUES IN INDUCTION
- CHAPTER 46. PROCESSING ISSUES IN COMPARISONS OF SYMBOLIC AND CONNECTIONIST LEARNING SYSTEMS
- CHAPTER 47. BACON, DATA ANALYSIS AND ARTIFICIAL INTELLIGENCE
-
Part 3: Learning Plan Knowledge
- CHAPTER 48. LEARNING TO PLAN IN COMPLEX DOMAINS
- CHAPTER 49. AN EMPIRICAL ANALYSIS OF EBL APPROACHES FOR LEARNING PLAN SCHEMATA
- CHAPTER 50. LEARNING DECISION RULES FOR SCHEDULING PROBLEMS: A CLASSIFIER HYBRID APPROACH
- CHAPTER 51. LEARNING TACTICAL PLANS FOR PILOT AIDING
- CHAPTER 52. ISSUES IN THE JUSTIFICATION-BASED DIAGNOSIS OF PLANNING FAILURES
- CHAPTER 53. LEARNING PROCEDURAL KNOWLEDGE IN THE EBG CONTEXT
- CHAPTER 54. LEARNING INVARIANTS FROM EXPLANATIONS
- Chapter 55. Using Learning to Recover Side-Effects of Operators in Robotics
- CHAPTER 56. LEARNING TO RECOGNIZE PLANS INVOLVING AFFECT
- Chapter 57. Learning to Retrieve Useful Information for Problem Solving
- CHAPTER 58. Discovering problem solving strategies: What humans do and machines don't (yet)
- Chapter 59. Approximating Learned Search Control Knowledge
- Chapter 60. Planning in Games Using Approximately Learned Macros
- CHAPTER 61. LEARNING APPROXIMATE PLANS FOR USE IN THE REAL WORLD
- Chapter 62. Using Concept Hierarchies to Organize Plan Knowledge
- CHAPTER 63. Conceptual Clustering of Mean-Ends Plans
- CHAPTER 64. LEARNING APPROPRIATE ABSTRACTIONS FOR PLANNING IN FORMATION PROBLEMS
- Chapter 65. Discovering Admissible Search Heuristics by Abstracting and Optimizing
- CHAPTER 66. LEARNING HIERARCHIES OF ABSTRACTION SPACES
- CHAPTER 67. LEARNING FROM OPPORTUNITY
- CHAPTER 68. LEARNING BY ANALYZING FORTUITOUS OCCURRENCES
- CHAPTER 69. EXPLANATION-BASED LEARNING OF REACTIVE OPERATORS
- CHAPTER 70. ON BECOMING REACTIVE
-
Part 4: Knowledge-Base Refinement and Theory Revision
- CHAPTER 71. KNOWLEDGE BASE REFINEMENT AND THEORY REVISION
- CHAPTER 72. THEORY FORMATION BY ABDUCTION: INITIAL RESULTS OF A CASE STUDY BASED ON THE CHEMICAL REVOLUTION
- CHAPTER 73. USING DOMAIN KNOWLEDGE TO AID SCIENTIFIC THEORY REVISION
- Chapter 74. The Role of Experimentation in Scientific Theory Revision
- CHAPTER 75. EXEMPLAR-BASED THEORY REJECTION: AN APPROACH TO THE EXPERIENCE CONSISTENCY PROBLEM
- CHAPTER 76. CONTROLLING SEARCH FOR THE CONSEQUENCES OF NEW INFORMATION DURING KNOWLEDGE INTEGRATION
- CHAPTER 77. IDENTIFYING KNOWLEDGE BASE DEFICIENCIES BY OBSERVING USER BEHAVIOR
- Chapter 78. Toward automated rational reconstruction: A case study
- CHAPTER 79. DISCOVERING MATHEMATICAL OPERATOR DEFINITIONS
- CHAPTER 80. IMPRECISE CONCEPT LEARNING WITHIN A GROWING LANGUAGE
- CHAPTER 81. USING DETERMINATIONS IN EBL: A SOLUTION TO THE INCOMPLETE THEORY PROBLEM
- Chapter 82. Some results on the complexity of knowledge-base refinement
- CHAPTER 83. KNOWLEDGE BASE REFINEMENT AS IMPROVING AN INCORRECT, INCONSISTENT AND INCOMPLETE DOMAIN THEORY
-
Part 5: Incremental Learning
- CHAPTER 84. INCREMENTAL LEARNING OF CONTROL STRATEGIES WITH GENETIC ALGORITHMS
- CHAPTER 85. TOWER OF HANOI WITH CONNECTIONIST NETWORKS: LEARNING NEW FEATURES
- Chapter 86. A Formal Framework for Learning in Embedded Systems
- Chapter 87. A Role for Anticipation in Reactive Systems that Learn
- CHAPTER 88. UNCERTAINTY BASED SELECTION OF LEARNING EXPERIENCES
- CHAPTER 89. IMPROVED TRAINING VIA INCREMENTAL LEARNING
- CHAPTER 90. INCREMENTAL BATCH LEARNING
- CHAPTER 91. INCREMENTAL CONCEPT FORMATION WITH COMPOSITE OBJECTS
- CHAPTER 92. USING MULTIPLE REPRESENTATIONS TO IMPROVE INDUCTIVE BIAS: GRAY AND BINARY CODING FOR GENETIC ALGORITHMS
- CHAPTER 93. FOCUSED CONCEPT FORMATION
- CHAPTER 94. An Exploration into Incremental Learning: the INFLUENCE system
- CHAPTER 95. INCREMENTAL, INSTANCE-BASED LEARNING OF INDEPENDENT AND GRADED CONCEPT DESCRIPTIONS
- Chapter 96. Cost-Sensitive Concept Learning of Sensor Use in Approach and Recognition
- CHAPTER 97. REDUCING REDUNDANT LEARNING
- CHAPTER 98. INCREMENTAL CLUSTERING BY MINIMIZING REPRESENTATION LENGTH
- CHAPTER 99. INFORMATION FILTERS AND THEIR IMPLEMENTATION IN THE SYLLOG SYSTEM
- CHAPTER 100. ADAPTIVE LEARNING OF DECISION-THEORETIC SEARCH CONTROL KNOWLEDGE
- CHAPTER 101. ADAPTIVE STRATEGIES OF LEARNING A STUDY OF TWO-PERSON ZERO-SUM COMPETITION
- CHAPTER 102. AN INCREMENTAL GENETIC ALGORITHM FOR REAL-TIME LEARNING
- CHAPTER 103. PARTICIPATORY LEARNING: A CONSTRUCTIVIST MODEL
-
Part 6: Representational Issues in Machine Learning
- CHAPTER 104. REPRESENTATIONAL ISSUES IN MACHINE LEARNING
- CHAPTER 105. Labor Saving New Distinctions
- CHAPTER 106. A THEORY OF JUSTIFIED REFORMULATIONS
- CHAPTER 107. REFORMULATION PROM STATE SPACE TO REDUCTION SPACE
- CHAPTER 108. KNOWLEDGE-BASED FEATURE GENERATION
- CHAPTER 109. ENRICHING VOCABULARIES BY GENERALIZING EXPLANATION STRUCTURES
- Chapter 110. Higher-Order and Modal Logic as a Framework for Explanation-Based Generalization
- CHAPTER 111. Towards A Formal Analysis of EBL
- CHAPTER 112. A MATHEMATICAL FRAMEWORK FOR STUDYING REPRESENTATION
- Chapter 113. Refining Representations to Improve Problem Solving Quality
- CHAPTER 114. COMPARING SYSTEMS AND ANALYZING FUNCTIONS TO IMPROVE CONSTRUCTIVE INDUCTION
- CHAPTER 115. EVALUATING ALTERNATIVE INSTANCE REPRESENTATIONS
- CHAPTER 116. EVALUATING BIAS DURING PAC-LEARNING
- CHAPTER 117. CONSTRUCTING REPRESENTATIONS USING INVERTED SPACES
- CHAPTER 118. A CONSTRUCTIVE INDUCTION FRAMEWORK
- CHAPTER 120. CONCEPT DISCOVERY THROUGH UTILIZATION OF INVARIANCE EMBEDDED IN THE DESCRIPTION LANGUAGE
- CHAPTER 121. DECLARATIVE BIAS FOR STRUCTURAL DOMAINS
- Chapter 122. Automatic Construction of a Hierarchical Generate-and-Test Algorithm
- Chapter 123. A Knowledge-level Analysis of Informing
- CHAPTER 124. AN OBJECT-ORIENTED REPRESENTATION FOR SEARCH ALGORITHMS
- CHAPTER 125. COMPILING LEARNING VOCABULARY FROM A PERFORMANCE SYSTEM DESCRIPTION
- CHAPTER 126. GENERALIZED RECURSIVE SPLITTING ALGORITHMS FOR LEARNING HYBRID CONCEPTS
- CHAPTER 127. SCREENING HYPOTHESES WITH EXPLICIT BIAS
- CHAPTER 128. BUILDING A LEARNING BIAS FROM PERCEIVED DEPENDENCIES
- CHAPTER 129. A BOOTSTRAPPING APPROACH TO CONCEPTUAL CLUSTERING
- CHAPTER 130. OVERCOMING FEATURE SPACE BIAS IN A REACTIVE ENVIRONMENT
- AUTHOR INDEX
Product information
- Title: Machine Learning Proceedings 1989
- Author(s):
- Release date: April 2016
- Publisher(s): Morgan Kaufmann
- ISBN: 9781483297408
You might also like
book
The Linux Philosophy for SysAdmins: And Everyone Who Wants To Be One
Reveals and illustrates the awesome power and flexibility of the command line, and the design and …
book
Python for Geospatial Data Analysis
In spatial data science, things in closer proximity to one another likely have more in common …
book
What Are ChatGPT and Its Friends?
ChatGPT is not your run-of-the-mill automated chat server. It's much more. But if you want to …
book
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 3rd Edition
Through a recent series of breakthroughs, deep learning has boosted the entire field of machine learning. …