icml 1993 论文列表Machine Learning, Proceedings of the Tenth International Conference, University of Massachusetts, Amherst, MA, USA, June 27-29, 1993. |
Better Learners Use Analogical Problem Solving Sparingly.
Multi-Agent Reinforcement Learning: Independent versus Cooperative Agents.
Learning from Queries and Examples with Tree-structured Bias.
Online Learning with Random Representations.
ATM Scheduling with Queuing Dely Predictions.
A Reinforcement Learning Method for Maximizing Undiscounted Rewards.
Compiling Bayesian Networks into Neural Networks.
Efficiently Inducing Determinations: A Complete and Systematic Search Algorithm that Uses Optimal Pruning.
Density-Adaptive Learning and Forgetting.
An SE-tree based Characterization of the Induction Problem.
Adaptive NeuroControl: How Black Box and Simple can it be.
Lookahead Feature Construction for Learning Hard Concepts.
Data Mining of Subjective Agricultural Data.
Combining Instance-Based and Model-Based Learning.
Explaining and Generalizing Diagnostic Decisions.
Learning DNF Via Probabilistic Evidence Combination.
Decision Theoretic Subsampling for Induction on Large Databases.
Combinatorial Optimization in Inductive Concept Learning.
Explanation Based Learning: A Comparison of Symbolic and Neural Network Approaches.
Overcoming Incomplete Perception with Utile Distinction Memory.
Scaling Up Reinforcement Learning for Robot Control.
Constraining Learning with Search Control.
Hierarchical Learning in Stochastic Domains: Preliminary Results.
Supervised Learning and Divide-and-Conquer: A Statistical Approach.
Generalization under Implication by Recursive Anti-unification.
Learning Procedures from Interactive Natural Language Instructions.
Learning Search Control Knowledge for Deep Space Network Scheduling.
Efficient Domain-Independent Experimentation.
Learning From Entailment: An Application to Propositional Horn Sentences.
SKICAT: A Machine Learning System for Automated Cataloging of Large Scale Sky Surveys.
Synthesis of Abstraction Hierarchies for Constraint Satisfaction by Clustering Approximately Equivalent Objects.
Discovering Dynamics.
Concept Sharing: A Means to Improve Multi-Concept Learning.
Small Disjuncts in Action: Learning to Diagnose Errors in the Local Loop of the Telephone Network.
Learning Symbolic Rules Using Artificial Neural Networks.
Constructing Hidden Variables in Bayesian Networks via Conceptual Clustering.
Automating Path Analysis for Building Causal Models from Data.
Using Qualitative Models to Guide Inductive Learning.
Multitask Learning: A Knowledge-Based Source of Inductive Bias.
GALOIS: An Order-Theoretic Approach to Conceptual Clustering.
Using Decision Trees to Improve Case-Based Learning.
Automatic Algorith/Model Class Selection.
ÉLÉNA: A Bottom-Up Learning Method.
The Evolution of Gennetic Algorithms: Towards Massive Parallelism.