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9th ML 1992: Aberdeen, Scotland, UK
- Derek H. Sleeman, Peter Edwards:
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 H. 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
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 - R. 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 H. Muggleton, Ashwin Srinivasan, Michael Bain:
Compression, Significance, and Accuracy. 338-347 - Yves Niquil:
Guiding Example Acquisition by Generating Scenarios. 348-354 - Arlindo L. Oliveira
Constructive Induction Using a Non-Greedy Strategy for Feature Selection. 355-360 - Christian W. Omlin
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 B. 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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