icml 1994 论文列表Machine Learning, Proceedings of the Eleventh International Conference, Rutgers University, New Brunswick, NJ, USA, July 10-13, 1994. |
Frequencies vs. Biases: Machine Learning Problems in Natural Language Processing - Abstract.
Bayesian Inductive Logic Programming.
A Statistical Approach to Decision Tree Modeling.
Selective Reformulation of Examples in Concept Learning.
Combining Top-down and Bottom-up Techniques in Inductive Logic Programming.
Small Sample Decision tree Pruning.
A Powerful Heuristic for the Discovery of Complex Patterned Behaviour.
An Improved Algorithm for Incremental Induction of Decision Trees.
A Modular Q-Learning Architecture for Manipulator Task Decomposition.
A Bayesian Framework to Integrate Symbolic and Neural Learning.
Prototype and Feature Selection by Sampling and Random Mutation Hill Climbing Algorithms.
Learning Without State-Estimation in Partially Observable Markovian Decision Processes.
A Constraint-based Induction Algorithm in FOL.
On the Worst-Case Analysis of Temporal-Difference Learning Algorithms.
A Conservation Law for Generalization Performance.
Hierarchical Self-Organization in Genetic programming.
Towards a Better Understanding of Memory-based Reasoning Systems.
The Minimum Description Length Principle and Categorical Theories.
Incremental Multi-Step Q-Learning.
Reducing Misclassification Costs.
Using Genetic Search to Refine Knowledge-based Neural Networks.
Revision of Production System Rule-Bases.
Efficient Algorithms for Minimizing Cross Validation Error.
Reward Functions for Accelerated Learning.
Comparing Methods for Refining Certainty-Factor Rule-Bases.
To Discount or Not to Discount in Reinforcement Learning: A Case Study Comparing R Learning and Q Learning.
Markov Games as a Framework for Multi-Agent Reinforcement Learning.
Heterogeneous Uncertainty Sampling for Supervised Learning.
Getting the Most from Flawed Theories.
An Efficient Subsumption Algorithm for Inductive Logic Programming.
Irrelevant Features and the Subset Selection Problem.
Rule Induction for Semantic Query Optimization.
Consideration of Risk in Reinforcement Learning.
Learning Disjunctive Concepts by Means of Genetic Algorithms.
Learning by Experimentation: Incremental Refinement of Incomplete Planning Domains.
An Incremental Learning Approach for Completable Planning.
Incremental Reduced Error Pruning.
In Defense of C4.5: Notes in Learning One-Level Decision Trees.
Boosting and Other Machine Learning Algorithms.
The Generate, Test, and Explain Discovery System Architecture.
Using Sampling and Queries to Extract Rules from Trained Neural Networks.
Greedy Attribute Selection.
Improving Accuracy of Incorrect Domain Theories.
Learning Recursive Relations with Randomly Selected Small Training Sets.
A New Method for Predicting Protein Secondary Structures Based on Stochastic Tree Grammars.