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Machine Learning Proceedings 1995 - Proceedings of the Twelfth International Conference on Machine Learning, Tahoe City, California, July 9-12 1995
von: Machine Learning
Elsevier Reference Monographs, 2014
ISBN: 9781483298665 , 606 Seiten
Format: PDF
Kopierschutz: DRM
Preis: 54,95 EUR
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Machine Learning Proceedings 1995 - Proceedings of the Twelfth International Conference on Machine Learning, Tahoe City, California, July 9-12 1995
Front Cover
1
Machine Learning
2
Copyright Page
3
Table of Contents
4
Preface
10
Advisory Committee
11
Program Committee
11
Auxiliary Reviewers
12
Workshops
12
Tutorials
12
PART 1: CONTRIBUTED PAPERS
16
Chapter 1. On-line Learning of Binary Lexical Relations Using Two-dimensional Weighted Majority Algorithms
18
ABSTRACT
18
1 Introduction
18
2 On-line Learning Model for Binary Relations
20
3 Two-dimensional Weighted Majority Prediction Algorithms
20
4 Experimental Results
21
5 Theoretical Performance Analysis
23
6 Concluding Remarks
26
Acknowledgement
26
References
26
Chapter 2. On Handling Tree-Structured Attributes in Decision Tree Learning
27
Abstract
27
1 Introduction
27
2 Decision Trees With Tree-Structured Attributes
28
3 Pre-processing Approaches
29
4 A Direct Approach
30
5 Analytical Comparison
31
6 Experimental Comparison
33
7 Summary and Conclusion
34
Acknowledgement
35
References
35
Chapter 3. Theory and Applications of Agnostic PAC-Learning with Small Decision Trees
36
Abstract
36
1 INTRODUCTION
36
2 THE AGNOSTIC PAC-LEARNING ALGORITHM T2
38
3 EVALUATION OF T2 ON "REAL-WORLD" CLASSIFICATION PROBLEMS
40
4 LEARNING CURVES FOR DECISION TREES OF SMALL DEPTH
42
5 CONCLUSION
43
Acknowledgement
43
References
44
Chapter 4. Residual Algorithms: Reinforcement Learning with Function Approximation
45
ABSTRACT
45
1 INTRODUCTION
45
2 ALGORITHMS FOR LOOKUP TABLES
46
3 DIRECT ALGORITHMS
46
4 RESIDUAL GRADIENT ALGORITHMS
47
5 RESIDUAL ALGORITHMS
48
6 STOCHASTIC MDPS AND MODELS
50
7 MDPS WITH MULTIPLE ACTIONS
50
8 RESIDUAL ALGORITHM SUMMARY
50
9 SIMULATION RESULTS
51
10 CONCLUSIONS
52
Acknowledgments
52
References
52
Chapter 5. Removing the Genetics from the Standard Genetic Algorithm
53
Abstract
53
1. THE GENETIC ALGORITHM (GA)
53
2. FOUR PEAKS: A PROBLEM DESIGNED TO BE GA-FRIENDLY
54
3. SELECTING THE GA'S PARAMETERS
55
4. POPULATION-BASED INCREMENTAL LEARNING
56
5. EMPIRICAL ANALYSIS ON THE FOUR PEAKS PROBLEM
57
6. DISCUSSION
59
7. CONCLUSIONS
60
ACKNOWLEDGEMENTS
60
REFERENCES
60
Chapter 6. Inductive Learning of Reactive Action Models
62
Abstract
62
1 INTRODUCTION
62
2 CONTEXT OF THE LEARNER
62
3 ACTIONS AND TELEO-OPERATORS
63
4 COLLECTING INSTANCES FOR LEARNING
64
5 THE INDUCTIVE LOGIC PROGRAMMING ALGORITHM
65
6 EVALUATION
66
7 RELATED WORK
67
8 FUTURE WORK
68
Acknowledgements
68
References
68
Chapter 7. Visualizing High-Dimensional Structure with the Incremental Grid Growing Neural Network
70
Abstract
70
1 INTRODUCTION
70
2 INCREMENTAL GRID GROWING
71
3 COMPARISON USING MINIMUM SPANNING TREEDATA
73
4 DEMONSTRATION USING REALWORLD SEMANTIC DATA
73
5 DISCUSSION AND FUTURE WORK
75
6 CONCLUSION
77
References
77
Chapter 8. Empirical support for Winnow and Weighted-Majority based algorithms: results on a calendar scheduling domain
79
Abstract
79
1 Introduction
79
2 The learning problem
80
3 Description of the algorithms
80
4 Experimental results
82
5 Theoretical results
85
Acknowledgements
87
References
87
Appendix
87
Chapter 9. Automatic Selection of Split Criterion during Tree Growing Based on Node Location
88
Abstract
88
1 DECISION TREE CONSTRUCTION
88
2 SITUATIONS IN WHICH ACCURACY IS THE BEST SPLITCRITERION
89
3 IMPLICATIONS FOR TREE-GROWING ALGORITHMS
90
4 EMPIRICAL SUPPORT OF THE HYPOTHESIS
90
5 FUTURE DIRECTIONS
94
References
94
Chapter 10. A Lexically Based Semantic Bias for Theory Revision
96
Abstract
96
1 INTRODUCTION
96
2 BACKGROUND
97
3 CLARUS
97
4 RESULTS
100
5 Discussion
103
6 CONCLUSION
104
Acknowledgments
104
References
104
Chapter 11. A Comparative Evaluation of Voting and Meta-learning on Partitioned Data
105
Abstract
105
1 Introduction
105
2 Common Voting and Statistical Techniques
105
3 Meta-learning Techniques
106
4 Experiments and Results
107
5 Arbiter Tree
110
6 Discussion
112
7 Concluding Remarks
112
References
113
Chapter 12. Fast and Efficient Reinforcement Learning with Truncated Temporal Differences
114
Abstract
114
1 INTRODUCTION
114
2 TD-BASED ALGORITHMS
115
3 TRUNCATED TEMPORAL DIFFERENCES
116
4 EXPERIMENTAL STUDIES
120
5 CONCLUSION
120
Acknowledgements
122
References
122
Chapter 13. K*: An Instance-based Learner Using an Entropie Distance Measure
123
Abstract
123
1 INTRODUCTION
123
2 ENTROPY AS A DISTANCE MEASURE
124
3 K* ALGORITHM
127
4 RESULTS
128
5 CONCLUSIONS
129
Acknowledgments
129
References
129
Chapter 14. Fast Effective Rule Induction
130
Abstract
130
1 INTRODUCTION
130
2 PREVIOUS WORK
130
3 EXPERIMENTS WITH IREP
132
4 IMPROVEMENTS TO IREP
134
5 CONCLUSIONS
137
References
138
Chapter 15. Chapter Text Categorization and Relational Learning
139
Abstract
139
1 INTRODUCTION
139
2 TEXT CATEGORIZATION
139
3 AN EXPERIMENTAL TESTBED
140
4 THE LEARNING METHOD
140
5 EVALUATING THERELATIONAL ENCODING
141
6 RELATION SELECTION
143
7 MONOTONICITY CONSTRAINTS
144
8 COMPARISON TO OTHER METHODS
145
9 CONCLUSIONS
146
Acknowledgements
146
References
147
Chapter 16. Protein Folding: Symbolic Refinement Competes with Neural Networks
148
Abstract
148
1 INTRODUCTION
148
2 THE PROTEIN FOLDING DOMAIN
148
3 RELATED WORK
150
4 KRUST'S SYMBOLIC REFINEMENT
151
5 EXPERIMENTAL RESULTS
153
6 SUMMARY
155
References
156
Chapter 17. A Bayesian Analysis of Algorithms for Learning Finite Functions
157
Abstract
157
1 Introduction
157
2 Preliminaries
158
3 Algorithms and priors
159
4 Approaches to prior and algorithm selection
161
5 Discussion and future work
162
Acknowledgements
164
References
164
Chapter 18. Committee-Based Sampling For Training Probabilistic Classifiers
165
Abstract
165
1 INTRODUCTION
165
2 BACKGROUND
166
3 COMMITTEE-BASEDSAMPLING
167
4 HMMS AND PART-OF-SPEECHTAGGING
168
5 COMMITTEE-BASEDSAMPLING FOR HMMS
168
6 EXPERIMENTAL RESULTS
170
7 CONCLUSIONS
171
References
171
Chapter 19. Learning Prototypical Concept Descriptions
173
Abstract
173
1 INTRODUCTION
173
2 LEARNING PROTOTYPICALDESCRIPTIONS
174
3 EVALUATION
176
4 DISCUSSION AND FUTUREDIRECTIONS
180
Acknowledgments
181
References
181
Chapter 20. A Case Study of Explanation-Based Control
182
Abstract
182
1 INTRODUCTION
182
2 THE ACROBOT
182
3 THE EBC APPROACH
183
4 A CONTROL THEORY SOLUTION
186
5 THE EBC SOLUTION
186
6 EMPIRICAL EVALUATION
188
7 CONCLUSIONS
189
Acknowledgements
190
References
190
Chapter 21. Explanation-Based Learning and Reinforcement Learning: A Unified View
191
Abstract
191
1 Introduction
191
2 Methods
193
3 Experiments and Results
196
4 Discussion
198
5 Conclusion
199
Acknowledgements
199
References
199
Chapter 22. Lessons from Theory Revision Applied to Constructive Induction
200
Abstract
200
1 Introduction
200
2 Context and Related Work
201
3 Demonstrations of Related Work
202
4 Theory-Guided Constructive Induction
205
5 Experiments
206
6 Discussion
207
References
208
Chapter 23. Supervised and Unsupervised Discretization of Continuous Features
209
Abstract
209
1 Introduction
209
2 Related Work
210
3 Methods
212
4 Results
213
5 Discussion
213
6 Summary
216
References
216
Chapter 24. Bounds on the Classification Error of the Nearest Neighbor Rule
218
Abstract
218
1 INTRODUCTION
218
2 DEFINITIONS AND THEOREMS
219
3 DISCUSSION AND CONCLUSION
222
Acknowledgements
222
References
222
Chapter 25. Q-Learning for Bandit Problems
224
Abstract
224
1 INTRODUCTION
224
2 BANDIT PROBLEMS
225
3 THE GITTINS INDEX
226
4 RESTART-IN-STATE-i PROBLEMS AND THE GITTINSINDEX
227
5 ON-LINE ESTIMATION OFGITTINS INDICES VIAQ-LEARNING
228
6 EXAMPLES
229
7 CONCLUSION
231
Acknowledgements
232
References
232
Chapter 26. Distilling Reliable Information From Unreliable Theories
233
Abstract
233
1 INTRODUCTION
233
2 IDENTIFYING STABLE EXAMPLES
233
3 USING STABILITY TO ELIMINATE NOISE
236
4 RESULTS
237
5 DISCUSSION
238
Acknowledgements
239
References
239
Chapter 27. A Quantitative Study of Hypothesis Selection
241
Abstract
241
1 Introduction
241
2 The Hypothesis Selection Problem
242
3 PAO Algorithms for Hypothesis Selection
242
4 Trading Off Exploitation and Exploration
245
5 Implication to Probabilistic Hill-Climbing
247
6 Related Work
248
7 Conclusion
248
Acknowledgements
249
References
249
Chapter 28. Learning proof heuristics by adapting parameters
250
Abstract
250
1 INTRODUCTION
250
2 FUNDAMENTALS
251
3 LEARNING PARAMETERS WITH A GA
252
4 THE UKB-PROCEDURE
253
5 DESIGNING A FITNESS FUNCTION
254
6 EXPERIMENTAL RESULTS
256
7 DISCUSSION
257
Acknowledgements
258
References
258
Chapter 29. Efficient Algorithms for Finding Multi-way Splits for Decision Trees
259
Abstract
259
1 Introduction
259
2 Computing Multi-Split Partitions
260
3 Experiments
262
4 Conclusion
265
Acknowledgements
266
References
266
Chapter 30. Ant-Q: A Reinforcement Learning approach to the traveling salesman problem
267
Abstract
267
1 INTRODUCTION
267
2 THE ANT-Q FAMILY OF ALGORITHMS
267
3 AN EXPERIMENTAL COMPARISONOF ANT-Q ALGORITHMS
268
4. TWO INTERESTING PROPERTIES OF ANT-Q
271
5 COMPARISONS WITH OTHER HEURISTICS AND SOME RESULTS ON DIFFICULT PROBLEMS
273
6 CONCLUSIONS
273
Acknowledgements
275
References
275
Chapter 31. Stable Function Approximation in Dynamic Programming
276
Abstract
276
1 INTRODUCTION AND BACKGROUND
276
2 DEFINITIONS AND BASIC THEOREMS
277
3 MAIN RESULTS: DISCOUNTED PROCESSES
278
4 NONDISCOUNTED PROCESSES
279
5 CONVERGING TO WHAT
281
6 EXPERIMENTS: HILL-CAR THE HARD WAY
281
7 CONCLUSIONS AND FURTHER RESEARCH
282
References
282
Chapter 32. The Challenge of Revising an Impure Theory
284
Abstract
284
1 Introduction
284
2 Framework
285
3 Computational Complexity
287
4 Prioritizing Default Theories
289
5 Conclusion
290
References
291
Chapter 33. Symbiosis in Multimodal Concept Learning
293
Abstract
293
1 INTRODUCTION
293
2 NICHE TECHNIQUES
294
3 SYSTEM OVERVIEW
294
4 INDIVIDUAL AND GROUP OPERATORS
296
5 FITNESS FUNCTION
297
6 COMPARISONS TO OTHER SYSTEMS
297
7 RESULTS
298
8 CONCLUSIONS
299
Acknowledgements
299
References
300
Chapter 34. Tracking the Best Expert
301
Abstract
301
1 INTRODUCTION
301
2 PRELIMINARIES
303
3 THE ALGORITHMS
303
4 FIXED SHARE ANALYSIS
304
5 VARIABLE SHARE ANALYSIS
305
6 EXPERIMENTAL RESULTS
308
References
309
Chapter 35. Reinforcement Learning by Stochastic Hill Climbing on Discounted Reward
310
Abstract
310
1 Introduction
310
2 Domain
310
3 Difficulties of Q-learning
312
4 Hill Climbing for Reinforcement Learning
312
5 Experiments
314
6 Discussion
316
7 Conclusion
316
Appendix
317
References
318
Chapter 36. Automatic Parameter Selection by Minimizing Estimated Error
319
Abstract
319
1 Introduction
319
2 The Parameter Selection Problem
320
3 The Wrapper Method
321
4 Automatic Parameter Selection for C4.5
322
5 Experiments with C4.5-AP
322
6 Related Work
325
7 Conclusion
326
Acknowledgments
326
References
326
Chapter 37. Error-Correcting Output Coding Corrects Bias and Variance
328
Abstract
328
1 Introduction
328
2 Definitions and Previous Work
329
3 Decomposing the Error Rate into Bias and Variance Components
331
4 ECOC and Voting
332
5 ECOC Reduces Variance and Bias
334
6 Bias Differences are Caused by Non-Local Behavior
334
7 Discussion and Conclusions
335
Acknowledgements
336
References
336
Chapter 38. Learning to Make Rent-to-Buy Decisions with Systems Applications
337
Abstract
337
1 Introduction
337
2 Definitions and Main Analytical Results
339
3 Algorithm Ae
339
4 Analysis
340
5 Adaptive Disk Spindown andRent-to-Buy
343
6 Experimental Results
343
Acknowledgements
344
References
344
Chapter 39. NewsWeeder: Learning to Filter Netnews
346
Abstract
346
1 INTRODUCTION
346
2 APPROACH
347
3 RESULTS
350
4 CONCLUSION
353
5 FUTURE WORK
353
Acknowledgments
353
References
353
Chapter 40. Hill Climbing Beats Genetic Search on a Boolean Circuit Synthesis Problem of Koza's
355
Abstract
355
1 Introduction
355
2 Genetic Programming
355
3 GP vs RGAT
356
4 Hill Climbing
356
5 Interpretation and Speculation
356
6 References
357
Chapter 41. Case-Based Acquisition of Place Knowledge
359
Abstract
359
1. Introduction and Basic Concepts
359
2. The Evidence Grid Representation
360
3. Case-Based Recognition of Places
361
4. Case-Based Learning of Places
362
5. Experiments with Place Learning
363
6. Related Work on Spatial Learning
365
7. Directions for Future Work
366
Acknowledgements
367
References
367
Chapter 42. Comparing Several Linear-threshold Learning Algorithms on Tasks Involving Superfluous Attributes
368
Abstract
368
1 INTRODUCTION
368
2 THE LEARNING TASKS
369
3 THE ALGORIT
369
4 DESCRIPTION OF THE PLOTS
371
5 CHECKING PROCEDURES
371
6 OBSERVATIONS
375
7 CONCLUSION
376
Chapter 43. Learning policies for partially observable environments: Scaling up
377
Abstract
377
1 INTRODUCTION
377
2 PARTIALLY OBSERVABLE MARKOV DECISION PROCESSES
378
3 SOME SOLUTION METHODS FOR POMDP's
379
4 HANDLING LARGER POMDP's: A HYBRID APPROACH
381
5 MORE ADVANCED REPRESENTATIONS
383
References
384
Chapter 44. Increasing the performance and consistency of classification trees by using the accuracy criterion at the leaves
386
Abstract
386
1 Introduction and Outline
386
2 Comparison of accuracy characteristics of split criteria
387
3 Revised Tree Growing Strategy
388
4 Empirical Results with revised strategy
389
Acknowledgements
390
References
390
Chapter 45. Efficient Learning with Virtual Threshold Gates
393
Abstract
393
1 Introduction
393
2 Preliminaries
395
3 The Winnow algorithms
395
4 Efficient On-line Learning of Simple Geometrical Objects When Dimension is Variable
396
5 Efficient On-line Learning of Simple Geometrical Objects When Dimension is Fixed
399
6 Conclusions
399
Acknowledgements
400
References
400
Chapter 46. Instance-Based Utile Distinctions for Reinforcement Learning with Hidden State
402
Abstract
402
1 INTRODUCTION
402
2 UTILE SUFFIX MEMORY
404
3 DETAILS OF THE ALGORITHM
404
4 EXPERIMENTAL RESULTS
406
5 RELATED WORK
409
6 DISCUSSION
409
Acknowledgments
410
References
410
Chapter 47. Efficient Learning from Delayed Rewards through Symbiotic Evolution
411
Abstract
411
1 Introduction
411
2 Neuro-Evolution
412
3 Symbiotic Evolution
412
4 The SANE Method
412
5 The Inverted Pendulum Problem
413
6 Population Dynamics in SANE
417
7 Related Work
417
8 Extending SANE
418
9 Conclusion
418
Acknowledgments
418
References
418
Chapter 48. Free to Choose: Investigating the Sample Complexity of Active Learning of Real Valued Functions
420
Abstract
420
1 INTRODUCTION
420
2 MODEL AND PRELIMINARIES
421
3 COLLECTING EXAMPLES: SAMPLING STRATEGIES
421
4 EXAMPLE 1: MONOTONIC FUNCTIONS
422
5 EXAMPLE 2: A CLASS WITH BOUNDED FIRST DERIVATIVE
424
6 CONCLUSIONS AND EXTENSIONS
426
Acknowledgements
426
References
426
Chapter 49. On learning Decision Committees
428
Abstract
428
1 Introduction
428
2 Definitions and theoretical results
429
3 Learning by DC{-i,0,i}: the IDC algorithm
430
4 Experiments
432
5 Discussion
433
References
434
Chapter 50. Inferring Reduced Ordered Decision Graphs of Minimum Description Length
436
Abstract
436
1 INTRODUCTION
436
2 DECISION TREES AND DECISION GRAPHS
436
3 MANIPULATING DISCRETE FUNCTIONS USING RODGS
437
4 MINIMUM MESSAGE LENGTH AND ENCODING OF RODGS
438
5 DERIVING AN RODG OF MINIMAL COMPLEXITY
439
6 EXPERIMENTS
442
7 CONCLUSIONS AND FUTURE WORK
444
References
444
Chapter 51. On Pruning and Averaging Decision Trees
445
Abstract
445
1 INTRODUCTION
445
2. OPTIMAL PRUNING
445
3 TREE AVERAGING
445
4 WEIGHTS FOR DECISION TREES
447
5 COMPLEXITY OF FANNING
448
6 COMPARISON OF AVERAGING AND PRUNING
449
7 DISCUSSION
450
8 FANNING OVER GRAPHS AND PRODUCTION RULES
451
9 CONCLUSION
451
References
452
Chapter 52. Efficient Memory-Based Dynamic Programming
453
Abstract
453
1 INTRODUCTION
453
2 MEMORY-BASED APPROACH
454
3 EXPERIMENTAL DEMONSTRATION
457
4 DISCUSSION
459
5 CONCLUSION
460
Acknowledgements
460
References
460
Chapter 53. Using Multidimensional Projection to Find Relations
462
Abstract
462
1 MOTIVATION
462
2 BASIC NOTIONS: RELATION AND PROJECTION
463
3 MULTIDIMENSIONAL RELATIONAL PROJECTION
463
4 A PROTOTYPE IMPLEMENTATION: MRP
464
5 EXPERIMENTAL RESULTS
466
6 RELATED RESEARCH
469
7 CONCLUSIONS
469
Acknowledgements
470
References
470
Chapter 54. Compression-Based Discretization of Continuous Attributes
471
Abstract
471
1 INTRODUCTION
471
2 AN MDL MEASURE FOR DISCRETIZED ATTRIBUTES
472
3 ALGORITHMIC USAGE
473
4 EXPERIMENTS AND EMPIRICAL RESULTS
474
5 CONCLUSIONS AND FURTHER RESEARCH
477
Acknowledgements
478
References
478
Chapter 55. MDL and Categorical Theories (Continued)
479
Abstract
479
1 INTRODUCTION
479
2 CLASS DESCRIPTION THEORIES AND MDL
480
3 AN ANOMALY AND A PREVIOUS SOLUTION
481
4 A NEW SOLUTION
481
5 APPLYING THE SCHEME TO C4.5RULES
482
6 RELATED RESEARCH
483
7 CONCLUSION
484
References
484
Chapter 56. For Every Generalization Action, Is There Reallyan Equal and Opposite Reaction? Analysis of the Conservation Law for Generalization Performance
486
Abstract
486
1 INTRODUCTION
486
2 CONSERVATION LAWREVISITED
486
3 AN ALTERNATE MEASURE OF GENERALIZATION
489
4 DISCUSSION
492
Acknowledgments
493
References
493
Chapter 57. Active Exploration and Learning in Real-Valued Spaces using Multi-Armed Bandit Allocation Indices
495
Abstract
495
1 Introduction and Motivation
495
2 Combining Classification Tree Algorithms with Gittins Indices
498
3 The Grasping Task
499
4 Discussion
500
5 Conclusion
501
Acknowledgments
501
References
502
Chapter 58. Discovering Solutions with Low Kolmogorov Complexity and High Generalization Capability
503
Abstract
503
1 INTRODUCTION
503
2 BASIC CONCEPTS
504
3 PROBABILISTIC SEARCH
505
4 "SIMPLE" NEURAL NETS
507
5 INCREMENTAL LEARNING
509
6 ACKNOWLEDGEMENTS
511
References
511
Chapter 59. A Comparison of Induction Algorithms for Selective andnon-Selective Bayesian Classifiers
512
Abstract
512
1 INTRODUCTION
512
2 NAIVE BAYESIAN CLASSIFIERS
513
3 BAYESIAN NETWORK CLASSIFIERS
513
5 DISCUSSION
516
6 RELATED WORK
518
7 CONCLUSION
519
Acknowledgement
520
References
520
Chapter 60. Retrofitting Decision Tree Classifiers Using Kernel Density Estimation
521
Abstract
521
1. INTRODUCTION
521
2 A REVIEW OF KERNEL DENSITY ESTIMATION
522
3 CLASSIFICATION WITH KERNEL DENSITY ESTIMATES
523
4 DECISION TREE DENSITY ESTIMATORS
523
5 DETAILS ON DECISION TREE DENSITY ESTIMATORS
524
6 EXPERIMENTAL RESULTS
524
7 RELATED WORK, EXTENSIONS, AND DISCUSSION
527
8 CONCLUSION
528
Chapter 61. Automatic Speaker Recognition: An Application of Machine Learning
530
Abstract
530
1 INTRODUCTION
530
2 PREPROCESSING
531
3 SPEAKER CLASSIFICATION
532
4 EXPERIMENTAL RESULTS
533
5 CONCLUSION
536
Acknowledgments
536
References
536
Chapter 62. An Inductive Learning Approach to Prognostic Prediction
537
Abstract
537
1 INTRODUCTION
537
2 RECURRENCE SURFACE APPROXIMATION
538
3 CLINICAL APPLICATION
542
4 CONCLUSIONS AND FUTURE WORK
544
Chapter 63. TD Models: Modeling the World at a Mixture of Time Scales
546
Abstract
546
1 Multi-Scale Planning and Modeling
546
2 Reinforcement Learning
547
3 The Prediction Problem
547
4 A Generalized Bellman Equation
548
5 n-Step Models
548
6 Intermixing Time Scales
548
7 ß-Models
549
8 Theoretical Results
550
9 TD(.) Learning of ß-models
551
10 A Wall-Following Example
551
11 A Hidden-State Example
552
12 Adding Actions (Future Work)
553
13 Conclusions
553
Acknowledgments
554
References
554
Chapter 64. Learning Collection Fusion Strategies for Information Retrieval
555
Abstract
555
1 INTRODUCTION
555
2 UNDERPINNINGS
556
3 LEARNING COLLECTION FUSION STRATEGIES
558
4 EXPERIMENTS
561
5 DISCUSSION AND CONCLUSIONS
562
References
563
Chapter 65. Learning by Observation and Practice:An Incremental Approach for Planning Operator Acquisition
564
Abstract
564
1 Introduction
564
2 Learning architecture overview
565
3 Issues of learning planning operators
565
4 Learning algorithm descriptions
567
5 Empirical results and analysis
570
Acknowledgements
571
References
572
Chapter 66. Learning with Rare Cases and Small Disjuncts
573
Abstract
573
1. INTRODUCTION
573
2. BACKGROUND
573
3. WHY ARE SMALL DISJUNCTS SO ERROR PRONE?
574
4. THE PROBLEM DOMAINS
574
5. THE EXPERIMENTS
575
6. RESULTS AND DISCUSSION
576
7. FUTURE RESEARCH
579
8. CONCLUSION
579
Acknowledgements
580
References
580
Chapter 67. Horizontal Generalization
581
Abstract
581
1 INTRODUCTION
581
2 FAN GENERALIZERS
582
3 COMPUTER EXPERIMENTS
582
4 GENERAL COMMENTS ON FG's
589
Acknowledgements
589
References
589
Chapter 68. Learning Hierarchies from Ambiguous Natural Language Data
590
Abstract
590
1 Introduction
590
2 Background
591
3 Learning Translation Rules with FOCL
591
4 Learning a Semantic Hierarchy from scratch
593
5 Updating an existing hierarchy
594
7 Limitation
597
8 Related Work
597
9 Conclusion
597
Acknowledgement
598
References
598
PART 2: INVITED TALKS
600
Chapter 69. Machine Learning and Information Retrieval
602
Chapter 70. Learning With Bayesian Networks
603
References
603
Chapter 71. Learning for Automotive Collision Avoidance and Autonomous Control
604
Author Index
606
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