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Machine Learning Proceedings 1993 - Proceedings of the Tenth International Conference on Machine Learning, University of Massachusetts, Amherst, June 27-29, 1993
von: Machine Learning
Elsevier Reference Monographs, 2014
ISBN: 9781483298627 , 361 Seiten
Format: PDF
Kopierschutz: DRM
Preis: 54,95 EUR
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Machine Learning Proceedings 1993 - Proceedings of the Tenth International Conference on Machine Learning, University of Massachusetts, Amherst, June 27-29, 1993
Front Cover
1
Machine Learning
2
Copyright Page
3
Table of Contents
4
PREFACE
8
ORGANIZING COMMITTEE
9
PROGRAM COMMITTEE
9
WORKSHOPS
10
Chapter 1. The Evolution of Genetic Algorithms: Towards Massive Parallelism
14
Abstract
14
1 INTRODUCTION
14
2 TRADITIONAL GAs
14
3 COARSE-GRAIN PARALLEL GAs
15
4 FINE-GRAIN PARALLEL GAs
16
5 FINE VS. COARSE-GRAIN PARALLELISIM
17
6 SUMMARY & FUTURE DIRECTIONS
20
References
21
CHAPTER 2. ÉLÉNA: A BOTTOM-UP LEARNING METHOD
22
ABSTRACT
22
INTRODUCTION
22
1 PRESENTATION OF THE SYSTEM
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2 THE LEARNING COMPONENT
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3 EXPERIMENTS
25
4 RELATED WORK
27
CONCLUSION
28
Acknowledgements
28
References
28
Chapter 3. Addressing the Selective Superiority Problem: Automatic Algorithm/Model Class Selection
30
Abstract
30
1 THE PROBLEM OF SELECTIVE SUPERIORITY
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2 AUTOMATIC ALGORITHM SELECTION
30
3 KNOWLEDGE-BASED SEARCH
31
4 RECURSIVE COMBINATION OF MODEL CLASSES
32
5 MCS: A MODEL CLASS SELECTION SYSTEM
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6 ILLUSTRATION
34
7 FUTURE WORK
36
8 CONCLUSION
37
Acknowledgments
37
References
37
Chapter 4. Using Decision Trees to Improve Case-Based Learning
38
Abstract
38
1 INTRODUCTION
38
2 LEARNING THE DEFINITION OF UNKNOWN WORDS
39
3 COMPARING THE DECISION TREE, CBL, AND HYBRID APPROACHES
40
4 RELATED WORK AND CONCLUSIONS
43
Acknowledgments
44
References
44
Chapter 5. GALOIS : An order-theoretic approach to conceptual clustering
46
Abstract
46
1 INTRODUCTION
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2 THE CONCEPT LATTICE: BACKGROUND
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3 AN ALGORITHM FOR THE INCREMENTAL DETERMINATION OF THE CONCEPT LATTICE
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4 COMPUTATIONAL COMPLEXITY
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5 EMPIRICAL EVALUATION OF GALOIS AS A LEARNING SYSTEM
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6 RELATED WORK
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7 CONCLUSION AND FUTURE WORK
53
Acknowledgements
53
References
53
Capter 6. Multitask Learning: A Knowledge-Based Source of Inductive Bias
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Abstract
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1 INTRODUCTION
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2 MULTITASK LEARNING AND INDUCTIVE BIAS
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3 AN EXAMPLE OF MULTITASK CONNECTIONIST LEARNING
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4 MULTITASK CONNECTIONIST LEARNING IN MORE DETAIL
58
5 MULTITASK DECISION TREES
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6 RELATED WORK
60
7 SUMMARY
60
Acknowledgements
61
References
61
Chapter 7. Using Qualitative Models to Guide Inductive Learning
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Abstract
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1 INTRODUCTION
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2 CONTEXT & RELATED WORK
63
3 LEARNING METHOD
63
4 EXPERIMENTAL EVALUATION
65
5 DISCUSSION AND CONCLUSION
69
References
69
Chapter 8. Automating Path Analysis for Building Causal Models from Data
70
Abstract
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1. INTRODUCTION
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2. BACKGROUND: REGRESSION
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3· PATH ANALYSIS
71
4. PATH ANALYSIS OF PHOENIX DATA
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5· AUTOMATIC GENERATION OF PATH MODELS
73
6. EXPERIMENTS
74
7. CONCLUSION
76
APPENDIX: DATA GENERATION
76
Acknowledgments
77
References
77
Chapter 9. Constructing Hidden Variables in Bayesian Networks via Conceptual Clustering
78
Abstract
78
1 INTRODUCTION
78
2 HIDDEN VARIABLES
78
3 LEARNING IN TANTRA
79
4 RESULTS
82
5 RELATED WORK
83
6 DISCUSSION
84
References
85
Chapter 10. Learning Symbolic Rules Using Artificial Neural Networks
86
Abstract
86
1 INTRODUCTION
86
2 EXTRACTING RULES FROM
2 EXTRACTING RULES FROM
87
87
3 EXTENDING NofM WITH SOFT WEIGHT-SHARING
88
4 DATA SETS
89
5 EXPERIMENTAL RESULTS
89
6 CONCLUSIONS
92
ACKNOWLEDGEMENTS
93
REFERENCES
93
Chapter 11. Small Disjuncts in Action: Learning to Diagnose Errors in the Local Loop of the Telephone Network
94
Abstract
94
1 INTRODUCTION
94
2 THE NYNEX MAX DOMAIN
95
3 C4.5 RESULTS
96
4 RL RESULTS
97
5 GENERALITY VS. ACCURACY
98
6 DISCUSSION: DISJUNCT SIZES AND NOISE
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7 CONCLUSIONS
101
References
101
Chapter 12. Concept Sharing: A Means to Improve Multi-Concept Learning
102
Abstract
102
1 Introduction
102
2 Relational Horn clause learning algorithms
102
3 Multiple concept FOCL
103
4 Evaluation
104
5 Related Work
108
6 Discussion
108
Acknowledgments
109
References
109
Chapter 13. Discovering Dynamics
110
Abstract
110
1 Introduction
110
2 The LAGRANGE Algorithm
111
3 Experimental evaluation
112
4 Related work
114
5 Discussion
114
References
115
Chapter 14. Synthesis of Abstraction Hierarchies for Constraint Satisfaction by Clustering Approximately Equivalent Objects
117
Abstract
117
1 Introduction
117
2 Abstract Search Spaces
118
3 Parameterized CSPs
119
4 Synthesis of Problem Solvers
119
5 Experimental Results
121
6 Future Work
122
7 Related Work
123
8 Summary
123
References
124
Chapter 15. SKICAT: A Machine Learning System for Automated Cataloging of Large Scale Sky Surveys
125
ABSTRACT
125
1. INTRODUCTION
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2. MACHINE LEARNING BACKGROUND
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3· CLASSIFYING SKY OBJECTS
127
4. CONCLUSIONS AND FUTURE WORK
131
REFERENCES
132
Chapter 16. Learning From Entailment: An Application to Prepositional Horn Sentences
133
Abstract
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1 INTRODUCTION
133
2 RELATED WORK
135
3 THE ALGORITHM
136
4 APPLICATION TOAPPROXIMATE ENTAILMENT
138
5 SUMIVIARY AND FUTUREWORK
139
Acknowledgments
140
References
140
Chapter 17. Efficient Domain-Independent Experimentation
141
Abstract
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1 Introduction
141
2 Learning by Experimentation
142
3 Domain-independent Heuristics for Efficient Experimentation
143
4 Results
144
5 Conclusion
145
Acknowledgments
146
References
147
Chapter 18. Learning Search Control Knowledge for Deep Space Network Scheduling
148
Abstract
148
1 INTRODUCTION
148
2 COMPOSER
149
3 THE DEEP SPACE NETWORK
149
4 EXPERIMENT AND RESULTS
152
5 DISCUSSION
153
Acknowledgements
154
References
154
Chapter 19. Learning procedures from interactive natural language instructions
156
Abstract
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1 INTRODUCTION
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2 RELATED WORK
157
3 INSTRUCTION WITHIN AN AUTONOMOUS AGENT
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4 LEARNING FROM INSTRUCTION
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5 EXAMPLE
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6 RESULTS
161
7 CONCLUSION
162
References
162
Chapter 20. Generalization under Implication by Recursive Anti-unification
164
Abstract
164
1 INTRODUCTION
164
2 PRELIMINARIES
165
3 GENERALIZATION BY RECURSIVE ANTI-UNIFICATION
166
4 RELATED WORK
170
5 CONCLUDING REMARKS
170
References
171
Chapter 21. Supervised learning and divide-and-conquer: A statistical approach
172
Abstract
172
1 INTRODUCTION
172
2 HIERARCHICAL MIXTURES OF EXPERTS
173
3 CONCLUSIONS
178
4 APPENDIX
179
Acknowledgements
179
References
179
Chapter 22. Hierarchical Learning in Stochastic Domains: Preliminary Results
180
Abstract
180
1 INTRODUCTION
180
2 Q AND DG LEARNING
180
3 LANDMARK NETWORKS
182
4 HDG LEARNING ALGORITHM
183
5 PRELIIMINARY EXFERIIMENTAL RESULT S
184
6 RELATED WORK
185
7 FUTURE WORK
185
References
186
Chapter 23. Constraining Learning with Search Control
187
Abstract
187
1 Introduction
187
2 Decisions Based on Lack of Knowledge
189
3 Experimental Results
190
4 Summary and Discussion
193
Acknowledgments
193
References
193
Chapter 24. Scaling Up Reinforcement Learning for Robot Control
195
Abstract
195
1 Introduction
195
2 The Learning Algorithm
195
3 The Domain: A Mobile Robot Simulator
196
4 A Docking Task and Teaching
197
5 Hierarchical Learning
198
6 Hidden State
200
Acknowledgements
202
References
202
Chapter 25. Overcoming Incomplete Perception with Utile Distinction Memory
203
Abstract
203
1 INTRODUCTION
203
2 UTILITY-BASED DISTINCTIONS FOR MEMORY
204
3 DETAILS OF THE ALGORITHM
204
4 EXPERIMENTAL RESULTS
207
5 CONCLUSIONS
207
References
208
Chapter 26. Explanation Based Learning: A Comparison of Symbolic and Neural Network Approaches
210
Abstract
210
1 Introduction
210
2 An Overview of EBNN
210
3 Correspondence between Symbolic and Neural Network EBL
213
4 Summary and Conclusions
216
Acknowledgments
217
References
217
Chapter 27. Combinatorial optimizationin in ductive concept learning
218
Abstract
218
1 INTRODUCTION
218
2 PROBLEM DEFINITION
219
3 COMBINATORIAL OPTIMIZATION ALGORITHMS USED FOR RULE INDUCTION
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4 ATRIS: A SHELL FOR RULEINDUCTION
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5 EXPERIMENTS AND RESULTS
221
6 CONCLUSION AND FURTHER WORK
223
Acknowledgements
223
References
223
Chapter 28. Decision Theoretic Subsampling for Induction on Large Databases
225
Abstract
225
1 INTRODUCTION
225
2 OVERVIEW
226
3 INFORMATION CONTENT DISTRIBUTIONS
227
4 EXPECTED LOSS
227
5 SAMPLING STRATEGY
228
6 EVALUATION
229
7 CONCLUSION
231
Acknowledgements
232
References
232
Chapter 29. Learning DNF Via Probabilistic Evidence Combination
233
Abstract
233
1 INTRODUCTION
233
2 LEARNING CONJUNCTIONS AS INCREMENTAL PROBABILISTIC EVIDENCE COMBINATION
234
3 EXAMPLES OF NOISE MODELS
235
4 LEARNING DNF FROM NOISY DATA
236
5 EXPERIMENTAL RESULTS
237
6 FUTURE WORK
239
7 SUMMARY
239
References
240
Chapter 30. Explaining and Generalizing Diagnostic Decisions
241
Abstract
241
1 EXPLAINING AND GENERALIZING DECISIONS
241
2 EMPIRICAL EVALUATION
243
3 ORDER OF MAGNITUDE REASONING
245
4 RELATED WORK
247
5 CONCLUSION
247
Acknowledgements
248
References
248
Chapter 31. Combining Instance-Based and Model-Based Learning
249
Abstract
249
1 INTRODUCTION
249
2 USING MODELS AND INSTANCES
249
3 EMPIRICAL EVALUATION
250
4 CONCLUSION
255
Acknowledgements
255
References
255
Chapter 32. Data Mining of Subjective Agricultural Data
257
Abstract
257
1 INTRODUCTION
257
2 OVERVIEW
258
3 STATISTICAL PROCESSING OF THE NTEP DATA
258
4 INITIAL STUDY: PREDICTING CULTIVAR PERFORMANCE
259
5 LEARNING MODELS FROM THE NTEP DATA
260
6 CONCLUSIONS
263
References
263
Chapter 33. Lookahead Feature Construction for Learning Hard Concepts
265
Abstract
265
1 Introduction
265
2 The LFC Algorithm
266
3 Empirical Results
268
4 Discussion and Related Work
270
5 Conclusion
271
Acknowledgments
272
References
272
Chapter 34. Adaptive Neuro Controi: How Black Box and Simple can it be
273
Abstract
273
1 INTRODUCTION
273
2 FROM NARENDRA'S APPROACH TO JORDAN'S APPROACH
274
3 THREE POSSIBLE EXTENSIONS OF JORDAN'S METHOD
276
4 COMPARISON OF THE FIVE METHODS
277
5 CONCLUSIONS
278
References
278
Chapter 35. An SE-tree based Characterization of the Induction Problem
281
Abstract
281
1 INTRODUCTION
281
2 A THEORY FOR INDUCTION
281
3 A LEARNING ALGORITHM
283
4 CLASSIFICATION ALGORITHMS
284
5 BIAS IN THE LEARNING PHASE
285
6 SE-TREE AND DECISION TREES
286
7 CONCLUSION AND FUTURE RESEARCH DIRECTIONS
287
Acknowledgements
288
References
288
Chapter 36. Density-Adaptive Learning and Forgetting
289
Abstract
289
1 Introduction and Motivation
289
2 Learning Algorithm
290
3 Density-Adaptive Forgetting
292
4 Conclusion and Future Extensions
294
Acknowledgements
296
References
296
Chapter 37. Efficiently Inducing Determinations: A Complete and Systematic Search Algorithm that Uses Optimal Pruning
297
Abstract
297
1 INTRODUCTION
297
2 RELATED WORK
297
3 VERIFYING A DETERMINATION
298
4 SEARCHING FOR DETERMINATIONS
300
5 CONCLUSIONS
303
Acknowledgements
303
References
303
Chapter 38. Compiling Bayesian Networks into Neural Networks
304
Abstract
304
1 Introduction
304
2 Bayesian Propagation Network Definition
305
3 Backpropagation
306
4 Representing Distributions
307
5 Empirical Evaluation of Generalization
308
6 Related Work
309
7 Conclusion
309
Acknowledgments
310
References
310
Chapter 39. A Reinforcement Learning Method for Maximizing Undiscounted Rewards
311
Abstract
311
1 Introduction
311
2 Background
311
3 IVf easures of Performance
312
4 The Connection Between Discounted andUndiscounted Value
314
5 Learning T-Optimal Policies
314
6 Advantages of R-Leaming
315
7 Experimental Results
317
8 Related Work
317
9 Conclusion
318
Acknowledgements
318
References
318
Chapter 40. ATM Scheduling with Queuing Delay Predictions
319
Abstract
319
Introduction
319
ATM Networking
319
On-Line Dynamic Programming
320
Experimental Evaluation
323
Simulations
324
Conclusions
325
Acknowledgements
325
References
326
Chapter 41. Online Learning with Random Representations
327
Abstract
327
1 Online Learning
327
2 Learning with Expanded Representations
328
3 A Basic RR Network
329
4 Performance vs Representation Size
330
5 Unsupervised Learning
331
6 Many Irrelevant Inputs
332
7 RR V S Backpropagation
332
8 Conclusions
333
Acknowledgments
334
References
334
Chapter 42. Learning from Queries and Examples with Tree-structured Bias
335
Abstract
335
1 Introduction
335
2 Tree-structured Bias
336
3 The PAC Learning Framework
336
4 The Learning Algorithm
337
5 Experimental Results
340
6 Discussion and Related Work
341
7 Conclusions and Future Work
342
Acknowledgments
342
References
342
Chapter 43. Multi-Agent Reinforcement Learning: Independent vs. Cooperative Agents
343
Abstract
343
1 INTRODUCTION
343
2 RELATED WORK
344
3 REINFORCEMENT LEARNING
344
4 TASK DESCRIPTION
345
5 CASE 1: SHARING SENSATION
345
6 CASE 2: SHARING POLICIES OR EPISODES
346
7 CASE 3: ON JOINT TASKS
348
8 CONCLUSIONS AND FUTURE WORK
349
Acknowledgments
350
References
350
Chapter 44. Better Learners Use Analogical Problem Solving Sparingly
351
Abstract
351
1 WHEN TO ANALOGIZE
351
2 GAP FILLING
352
3 AVOIDING ANALOGY
353
4 USING ANALOGY SPARINGLY
355
5 DISCUSSION
356
Acknowledgements
358
References
358
AUTHOR INDEX
359
SUBJECT INDEX
360
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