icml 1988 论文列表Machine Learning, Proceedings of the Fifth International Conference on Machine Learning, Ann Arbor, Michigan, USA, June 12-14, 1988. |
The Role of Forgetting in Learning.
Some Chunks Are Expensive.
Utilizing Experience for Improving the Tactical Manager.
Experimental Results from an Evaluation of Algorithms that Learn to Control Dynamic Systems.
Diffy-S: Learning Robot Operator Schemata from Examples.
Hypothesis Filtering: A Practical Approach to Reliable Learning.
Two New Frameworks for Learning.
Learning Systems of First-Order Rules.
Extending the Valiant Learning Model.
Reduction: A Practical Mechanism of Searching for Regularity in Data.
A Hill-Climbing Approach to Machine Discovery.
The Interdependencies of Theory Formation, Revision, and Experimentation.
Machine Invention of First Order Predicates by Inverting Resolution.
Theory Discovery and the Hypothesis Language.
Learning to Program by Examining and Modifying Cases.
A Knowledge Intensive Approach to Concept Induction.
An Approach Based on Integrated Learning to Generating Stories.
Integrated Learning with Incorrect and Incomplete Theories.
Using Experience-Based Learning in Game Playing.
Generalizing the Order of Operators in Macro-Operators.
Generalizing Number and Learning from Multiple Examples in Explanation Based Learning.
Active Explanation Reduction: An Approach to the Multiple Explanations Problem.
On the Tractability of Learning from Incomplete Theories.
Boundaries of Operationality.
Reasoning about Operationality for Explanation-Based Learning.
Building and Using Mental Models in a Sensory-Motor Domain.
Connectionist Learning of Expert Backgammon Evaluations.
Competitive Reinforcement Learning.
Some Interesting Properties of a Connectionist Inductive Learning System.
Midgard: A Genetic Approach to Adaptive Load Balancing for Distributed Systems.
Classifier Systems with Hamming Weights.
Representation and Hidden Bias: Gray vs. Binary Coding for Genetic Algorithms.
Population Size in classifier Systems.
An Empirical Comparison of Genetic and Decision-Tree Classifiers.
Using Weighted Networks to Represent Classification Knowledge in Noisy Domains.
ID5: An Incremental ID3.
Improved Decision Trees: A Generalized Version of ID3.
Experiments on the Costs and Benefits of Windowing in ID3.
Deferred Commitment in UNIMEM: Waiting to Learn.
Trading Off Simplicity and Coverage in Incremental concept Learning.
Incremental Multiple Concept Learning Using Experiments.
AutoClass: A Bayesian Classification System.
Conceptual Clumping of Binary Vectors with Occam's Razor.
Learning Categorical Decision Criteria in Biomedical Domains.
Learning Graph Models of Shape.
Concept Simplification and Prediction Accuracy.
On Asking the Right Questions.
Tuning Rule-Based Systems to Their Environments.
Using a Generalization Hierarchy to Learn from Examples.