Book description
Machine Learning Proceedings 1995
Table of contents
- Front Cover
- Machine Learning
- Copyright Page
- Table of Contents (1/2)
- Table of Contents (2/2)
- Preface
- Advisory Committee
- Program Committee
- Auxiliary Reviewers
- Workshops
- Tutorials
-
PART 1: CONTRIBUTED PAPERS
- Chapter 1. On-line Learning of Binary Lexical Relations Using Two-dimensional Weighted Majority Algorithms
- Chapter 2. On Handling Tree-Structured Attributes in Decision Tree Learning
- Chapter 3. Theory and Applications of Agnostic PAC-Learning with Small Decision Trees
- Chapter 4. Residual Algorithms: Reinforcement Learning with Function Approximation
- Chapter 5. Removing the Genetics from the Standard Genetic Algorithm
- Chapter 6. Inductive Learning of Reactive Action Models
- Chapter 7. Visualizing High-Dimensional Structure with the Incremental Grid Growing Neural Network
- Chapter 8. Empirical support for Winnow and Weighted-Majority based algorithms: results on a calendar scheduling domain
- Chapter 9. Automatic Selection of Split Criterion during Tree Growing Based on Node Location
- Chapter 10. A Lexically Based Semantic Bias for Theory Revision
- Chapter 11. A Comparative Evaluation of Voting and Meta-learning on Partitioned Data
- Chapter 12. Fast and Efficient Reinforcement Learning with Truncated Temporal Differences
- Chapter 13. K*: An Instance-based Learner Using an Entropie Distance Measure
- Chapter 14. Fast Effective Rule Induction
- Chapter 15. Chapter Text Categorization and Relational Learning
- Chapter 16. Protein Folding: Symbolic Refinement Competes with Neural Networks
- Chapter 17. A Bayesian Analysis of Algorithms for Learning Finite Functions
- Chapter 18. Committee-Based Sampling For Training Probabilistic Classifiers
- Chapter 19. Learning Prototypical Concept Descriptions
- Chapter 20. A Case Study of Explanation-Based Control
- Chapter 21. Explanation-Based Learning and Reinforcement Learning: A Unified View
- Chapter 22. Lessons from Theory Revision Applied to Constructive Induction
- Chapter 23. Supervised and Unsupervised Discretization of Continuous Features
- Chapter 24. Bounds on the Classification Error of the Nearest Neighbor Rule
- Chapter 25. Q-Learning for Bandit Problems
- Chapter 26. Distilling Reliable Information From Unreliable Theories
- Chapter 27. A Quantitative Study of Hypothesis Selection
- Chapter 28. Learning proof heuristics by adapting parameters
- Chapter 29. Efficient Algorithms for Finding Multi-way Splits for Decision Trees
- Chapter 30. Ant-Q: A Reinforcement Learning approach to the traveling salesman problem
- Chapter 31. Stable Function Approximation in Dynamic Programming
- Chapter 32. The Challenge of Revising an Impure Theory
- Chapter 33. Symbiosis in Multimodal Concept Learning
- Chapter 34. Tracking the Best Expert
- Chapter 35. Reinforcement Learning by Stochastic Hill Climbing on Discounted Reward
- Chapter 36. Automatic Parameter Selection by Minimizing Estimated Error
- Chapter 37. Error-Correcting Output Coding Corrects Bias and Variance
- Chapter 38. Learning to Make Rent-to-Buy Decisions with Systems Applications
- Chapter 39. NewsWeeder: Learning to Filter Netnews
- Chapter 40. Hill Climbing Beats Genetic Search on a Boolean Circuit Synthesis Problem of Koza's
- Chapter 41. Case-Based Acquisition of Place Knowledge
- Chapter 42. Comparing Several Linear-threshold Learning Algorithms on Tasks Involving Superfluous Attributes
- Chapter 43. Learning policies for partially observable environments: Scaling up
- Chapter 44. Increasing the performance and consistency of classification trees by using the accuracy criterion at the leaves
- Chapter 45. Efficient Learning with Virtual Threshold Gates
- Chapter 46. Instance-Based Utile Distinctions for Reinforcement Learning with Hidden State
- Chapter 47. Efficient Learning from Delayed Rewards through Symbiotic Evolution
- Chapter 48. Free to Choose: Investigating the Sample Complexity of Active Learning of Real Valued Functions
- Chapter 49. On learning Decision Committees
- Chapter 50. Inferring Reduced Ordered Decision Graphs of Minimum Description Length
- Chapter 51. On Pruning and Averaging Decision Trees
- Chapter 52. Efficient Memory-Based Dynamic Programming
- Chapter 53. Using Multidimensional Projection to Find Relations
- Chapter 54. Compression-Based Discretization of Continuous Attributes
- Chapter 55. MDL and Categorical Theories (Continued)
- Chapter 56. For Every Generalization Action, Is There Reallyan Equal and Opposite Reaction? Analysis of the Conservation Law for Generalization Performance
- Chapter 57. Active Exploration and Learning in Real-Valued Spaces using Multi-Armed Bandit Allocation Indices
- Chapter 58. Discovering Solutions with Low Kolmogorov Complexity and High Generalization Capability
- Chapter 59. A Comparison of Induction Algorithms for Selective andnon-Selective Bayesian Classifiers
- Chapter 60. Retrofitting Decision Tree Classifiers Using Kernel Density Estimation
- Chapter 61. Automatic Speaker Recognition: An Application of Machine Learning
- Chapter 62. An Inductive Learning Approach to Prognostic Prediction
-
Chapter 63. TD Models: Modeling the World at a Mixture of Time Scales
- Abstract
- 1 Multi-Scale Planning and Modeling
- 2 Reinforcement Learning
- 3 The Prediction Problem
- 4 A Generalized Bellman Equation
- 5 n-Step Models
- 6 Intermixing Time Scales
- 7 β-Models
- 8 Theoretical Results
- 9 TD(λ) Learning of β-models
- 10 A Wall-Following Example
- 11 A Hidden-State Example
- 12 Adding Actions (Future Work)
- 13 Conclusions
- Acknowledgments
- References
- Chapter 64. Learning Collection Fusion Strategies for Information Retrieval
- Chapter 65. Learning by Observation and Practice:An Incremental Approach for Planning Operator Acquisition
- Chapter 66. Learning with Rare Cases and Small Disjuncts
- Chapter 67. Horizontal Generalization
- Chapter 68. Learning Hierarchies from Ambiguous Natural Language Data
- PART 2: INVITED TALKS
- Author Index
Product information
- Title: Machine Learning Proceedings 1995
- Author(s):
- Release date: January 2016
- Publisher(s): Morgan Kaufmann
- ISBN: 9781483298665
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