14. ML 1992:
Aberdeen,
Scotland,
UK
Derek H. Sleeman, Peter Edwards (Eds.):
Proceedings of the Ninth International Workshop on Machine Learning (ML 1992), Aberdeen, Scotland, UK, July 1-3, 1992.
Morgan Kaufmann 1992, ISBN 1-55860-247-X 
- David W. Aha:
Generalizing from Case studies: A Case Study.
1-10
- Hussein Almuallim, Thomas G. Dietterich:
On Learning More Concepts.
11-19
- Jerzy W. Bala, Ryszard S. Michalski, Janusz Wnek:
The Principal Axes Method for Constructive Induction.
20-29
- Neeraj Bhatnagar:
Learning by Incomplete Explanation-Based Learning.
37-42
- Claudio Carpineto:
Trading Off Consistency and Efficiency in version-Space Induction.
43-48
- Jason Catlett:
Peepholing: Choosing Attributes Efficiently for Megainduction.
49-54
- Pang C. Chen:
Improving Path Planning with Learning.
55-61
- Peter C.-H. Cheng, Herbert A. Simon:
The Right Representation for Discovery: Finding the Conservation of Momentum.
62-71
- Alan D. Christiansen:
Learning to Predict in Uncertain Continuous Tasks.
72-81
- Peter Clark, Robert C. Holte:
Lazy Partial Evaluation: An Integration of Explanation-Based Generalization and Partial Evaluation.
82-91
- Jeffery A. Clouse, Paul E. Utgoff:
A Teaching Method for Reinforcement Learning.
92-110
- Darrell Conklin, Janice I. Glasgow:
Spatial Analogy and Subsumption.
111-116
- Timothy M. Converse, Kristian J. Hammond:
Learning to Satisfy Conjunctive Goals.
117-122
- Michael T. Cox, Ashwin Ram:
Multistrategy Learning with Introspective Meta-Explanations.
123-128
- Oren Etzioni:
An Asymptotic Analysis of Speedup Learning.
129-136
- Oren Etzioni, Steven Minton:
Why EBL Produces Overly-Specific Knowledge: A Critique of the PRODIGY Approaches.
137-143
- Tom Fawcett, Paul E. Utgoff:
Automatic Feature Generation for Problem Solving Systems.
144-153
- Cao Feng, Stephen Muggleton:
Towards Inductive Generalization in Higher Order Logic.
154-162
- Douglas H. Fisher, Ling Xu, Nazih Zard:
Ordering Effects in Clustering.
162-168
- Attilio Giordana, Claudio Sale:
Learning Structured Concepts Using Genetic Algorithms.
169-178
- Jonathan Gratch, Gerald DeJong:
An Analysis of Learning to Plan as a Search Problem.
179-188
- John J. Grefenstette, Connie Loggia Ramsey:
An Approach to Anytime Learning.
189-195
- Ray J. Hickey:
Artificial Universes - Towards a Systematic Approach to Evaluation Algorithms which Learn form Examples.
196-205
- Daniel S. Hirschberg, Michael J. Pazzani:
Average Case Analysis of Learning kappa-CNF Concepts.
206-211
- Elizabeth I. Hogger, Krysia Broda:
The MENTLE Approach to Learning Heuristics for the Control of Logic Programs.
212-217
- Lawrence B. Holder, Diane J. Cook, Horst Bunke:
Fuzzy Substructure Discovery.
218-223
- Lawrence Hunter, Nomi L. Harris, David J. States:
Efficient Classification of Massive, Unsegmented Datastreams.
224-232
- Wayne Iba, Pat Langley:
Induction of One-Level Decision Trees.
233-240
- Cezary Z. Janikow:
Combining Competition and Cooperation in Supervised Inductive Learning.
241-248
- Kenji Kira, Larry A. Rendell:
A Practical Approach to Feature Selection.
249-256
- Igor Kononenko, Matevz Kovacic:
Learning as Optimization: Stochastic Generation of Multiple Knowledge.
257-262
- Philip Laird:
Dynamic Optimization.
263-272
- Stephane Lapointe, Stan Matwin:
Sub-unification: A Tool for Efficient Induction of Recursive Programs.
273-281
- Rey-Long Liu, Von-Wun Soo:
Augmenting and Efficiently Utilizing Domain Theory in Explanation-Based Natural Language Acquisition.
282-289
- Sridhar Mahadevan:
Enhancing Transfer in Reinforcement Learning by Building Stochastic Models of Robot Actions.
290-299
- Chengjiang Mao:
THOUGHT: An Integrated Learning System for Acquiring Knowledge Structure.
300-309
- Zdravko Markov:
An Approach to Concept Learning Based on Term Generalization.
310-315
- Andrew McCallum:
Using Transitional Proximity for Faster Reinforcement Learning.
316-321
- Thierry Van de Merckt:
NFDT: A System that Learns Flexible Concepts Based on Decision Trees for Numerical Attributes.
322-331
- Marjorie Moulet:
A Symbolic Algorithm for Computing Coefficients' Accuracy in Regression.
332-337
- Stephen Muggleton, Ashwin Srinivasan, Michael Bain:
Compression, Significance, and Accuracy.
338-347
- Yves Niquil:
Guiding Example Acquisition by Generating Scenarios.
348-354
- Arlindo L. Oliveira, Alberto L. Sangiovanni-Vincentelli:
Constructive Induction Using a Non-Greedy Strategy for Feature Selection.
355-360
- Christian W. Omlin, C. Lee Giles:
Training Second-Order Recurrent Neural Networks using Hints.
361-366
- M. Alicia Pérez, Oren Etzioni:
DYNAMIC: A New Role for Training Problems in EBL.
367-372
- Ashvin Radiya, Jan M. Zytkow:
A Framework for Discovering Discrete Event Models.
373-378
- David Ruby, Dennis F. Kibler:
Learning Episodes for Optimization.
379-384
- Claude Sammut, Scott Hurst, Dana Kedzier, Donald Michie:
Learning to Fly.
385-393
- Cullen Schaffer:
Deconstructing the Digit Recognition Problem.
394-399
- Alberto Maria Segre:
On Combining Multiple Speedup Techniques.
400-405
- Satinder P. Singh:
Scaling Reinforcement Learning Algorithms by Learning Variable Temporal Resolution Models.
406-415
- Padhraic Smyth, Jeff Mellstrom:
Detecting Novel Classes with Applications to Fault Diagnosis.
416-425
- Devika Subramanian, Scott Hunter:
Measuring Utility and the Design of Provably Good EBL Algorithms.
426-425
- Somkiat Tangkitvanich, Masamichi Shimura:
Refining a Relational Theory with Multiple Faults in the Concept and Subconcepts.
436-444
- Gheorghe Tecuci:
Cooperation in Knowledge Base Refinement.
445-450
- Gerald Tesauro:
Temporal Difference Learning of Backgammon Strategy.
451-457
- Gilles Venturini:
AGIL: Solving the Exploration Versus Exploration Dilemma in a single Classifier System Applied to Simulated Robotics.
458-463
- Jerry B. Weinberg, Gautam Biswas, Glenn R. Koller:
Conceptual Clustering with Systematic Missing Values.
464-469
- Jianping Zhang:
Selecting Typical Instances in Instance-Based Learning.
470-479
- Jan M. Zytkow, Jieming Zhu, Robert Zembowicz:
The First Phase of Real-World Discovery: Determining Repeatability and Error of Experiments.
480-485
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