icml 1995 论文列表Machine Learning, Proceedings of the Twelfth International Conference on Machine Learning, Tahoe City, California, USA, July 9-12, 1995. |
Learning for Automotive Collision Avoidance and Autonomous Control.
Learning With Bayesian Networks (Abstract).
Machine Learning and Information Retrieval (Abstract).
Learning Hierarchies from Ambiguous Natural Language Data.
Horizonal Generalization.
Learning with Rare Cases and Small Disjuncts.
Learning by Observation and Practice: An Incremental Approach for Planning Operator Acquisition.
Learning Collection Fusion Strategies for Information Retrieval.
TD Models: Modeling the World at a Mixture of Time Scales.
An Inductive Learning Approach to Prognostic Prediction.
Automatic Speaker Recognition: An Application of Machine Learning.
Retrofitting Decision Tree Classifiers Using Kernel Density Estimation.
A Comparison of Induction Algorithms for Selective and non-Selective Bayesian Classifiers.
Discovering Solutions with Low Kolmogorov Complexity and High Generalization Capability.
Active Exploration and Learning in real-Valued Spaces using Multi-Armed Bandit Allocation Indices.
For Every Generalization Action, Is There Really an Equal and Opposite Reaction?
MDL and Categorical Theories (Continued).
Compression-Based Discretization of Continuous Attributes.
Using Multidimensional Projection to Find Relations.
Efficient Memory-Based Dynamic Programming.
On Pruning and Averaging Decision Trees.
Inferring Reduced Ordered Decision Graphs of Minimum Description Length.
On Learning Decision Committees.
Free to Choose: Investigating the Sample Complexity of Active Learning of Real Valued Functions.
Efficient Learning from Delayed Rewards through Symbiotic Evolution.
Instance-Based Utile Distinctions for Reinforcement Learning with Hidden State.
Efficient Learning with Virtual Threshold Gates.
Increasing the Performance and Consistency of Classification Trees by Using the Accuracy Criterion at the Leaves.
Learning Policies for Partially Observable Environments: Scaling Up.
Comparing Several Linear-threshold Learning Algorithms on Tasks Involving Superfluous Attributes.
Case-Based Acquisition of Place Knowledge.
Hill Climbing Beats Genetic Search on a Boolean Circuit Synthesis Problem of Koza's.
NewsWeeder: Learning to Filter Netnews.
Learning to Make Rent-to-Buy Decisions with Systems Applications.
Error-Correcting Output Coding Corrects Bias and Variance.
Automatic Parameter Selection by Minimizing Estimated Error.
Reinforcement Learning by Stochastic Hill Climbing on Discounted Reward.
Tracking the Best Expert.
Symbiosis in Multimodal Concept Learning.
The Challenge of Revising an Impure Theory.
Stable Function Approximation in Dynamic Programming.
Ant-Q: A Reinforcement Learning Approach to the Traveling Salesman Problem.
Efficient Algorithms for Finding Multi-way Splits for Decision Trees.
Learning Proof Heuristics by Adaptive Parameters.
A Quantitative Study of Hypothesis Selection.
Distilling Reliable Information From Unreliable Theories.
Q-Learning for Bandit Problems.
Bounds on the Classification Error of the Nearest Neighbor Rule.
Supervised and Unsupervised Discretization of Continuous Features.
Lessons from Theory Revision Applied to Constructive Induction.
Explanation-Based Learning and Reinforcement Learning: A Unified View.
A Case Study of Explanation-Based Control.
Learning Prototypical Concept Descriptions.
Committee-Based Sampling For Training Probabilistic Classifiers.
A Bayesian Analysis of Algorithms for Learning Finite Functions.
Protein Folding: Symbolic Refinement Competes with Neural Networks.
Text Categorization and Relational Learning.
Fast Effective Rule Induction.
K*: An Instance-based Learner Using and Entropic Distance Measure.
Fast and Efficient Reinforcement Learning with Truncated Temporal Differences.
A Comparative Evaluation of Voting and Meta-learning on Partitioned Data.
A Lexical Based Semantic Bias for Theory Revision.
Automatic Selection of Split Criterion during Tree Growing Based on Node Location.
Empirical Support for Winnow and Weighted-Majority Based Algorithms: Results on a Calendar Scheduling Domain.
Visualizing High-Dimensional Structure with the Incremental Grid Growing Neural Network.
Inductive Learning of Reactive Action Models.
Removing the Genetics from the Standard Genetic Algorithm.
Residual Algorithms: Reinforcement Learning with Function Approximation.
Theory and Applications of Agnostic PAC-Learning with Small Decision Trees.
On Handling Tree-Structured Attributed in Decision Tree Learning.
On-line Learning of Binary Lexical Relations Using Two-dimensional Weighted Majority Algorithms.