icml 1998 论文列表Proceedings of the Fifteenth International Conference on Machine Learning (ICML 1998), Madison, Wisconsin, USA, July 24-27, 1998. |
The Problem with Noise and Small Disjuncts.
Teaching an Agent to Test Students.
Intra-Option Learning about Temporally Abstract Actions.
Learning the Grammar of Dance.
A Neural Network Model for Prognostic Prediction.
Heading in the Right Direction.
Value Function Based Production Scheduling.
Ridge Regression Learning Algorithm in Dual Variables.
Automatic Segmentation of Continuous Trajectories with Invariance to Nonlinear Warpings of Time.
An Investigation of Transformation-Based Learning in Discourse.
Evolving Structured Programs with Hierarchical Instructions and Skip Nodes.
RL-TOPS: An Architecture for Modularity and Re-Use in Reinforcement Learning.
Learning First-Order Acyclic Horn Programs from Entailment.
Learning to Drive a Bicycle Using Reinforcement Learning and Shaping.
Theory Refinement of Bayesian Networks with Hidden Variables.
The Case against Accuracy Estimation for Comparing Induction Algorithms.
Classification Using Phi-Machines and Constructive Function Approximation.
A Randomized ANOVA Procedure for Comparing Performance Curves.
An Analysis of Direct Reinforcement Learning in Non-Markovian Domains.
On the Power of Decision Lists.
On Feature Selection: Learning with Exponentially Many Irrelevant Features as Training Examples.
Collaborative Filtering Using Weighted Majority Prediction Algorithms.
Q2: Memory-Based Active Learning for Optimizing Noisy Continuous Functions.
Stochastic Resonance with Adaptive Fuzzy Systems.
A Case Study in the Use of Theory Revision in Requirements Validation.
Improving Text Classification by Shrinkage in a Hierarchy of Classes.
Employing EM and Pool-Based Active Learning for Text Classification.
Multiple-Instance Learning for Natural Scene Classification.
Learning to Locate an Object in 3D Space from a Sequence of Camera Images.
Using Eligibility Traces to Find the Best Memoryless Policy in Partially Observable Markov Decision Processes.
Learning a Language-Independent Representation for Terms from a Partially Aligned Corpus.
Structural Machine Learning with Galois Lattice and Graphs.
An Information-Theoretic Definition of Similarity.
Using Learning for Approximation in Stochastic Processes.
An Analysis of Actor/Critic Algorithms Using Eligibility Traces: Reinforcement Learning with Imperfect Value Function.
A Fast, Bottom-Up Decision Tree Pruning Algorithm with Near-Optimal Generalization.
Near-Optimal Reinforcement Learning in Polynominal Time.
Coevolutionary Learning: A Case Study.
Multiagent Reinforcement Learning: Theoretical Framework and an Algorithm.
Solving a Huge Number of Similar Tasks: A Combination of Multi-Task Learning and a Hierarchical Bayesian Approach.
Well-Behaved Borgs, Bolos, and Berserkers.
A Learning Rate Analysis of Reinforcement Learning Algorithms in Finite-Horizon.
Local Cascade Generalization.
Multi-criteria Reinforcement Learning.
The Kernel-Adatron Algorithm: A Fast and Simple Learning Procedure for Support Vector Machines.
Bayesian Network Classification with Continuous Attributes: Getting the Best of Both Discretization and Parametric Fitting.
An Efficient Boosting Algorithm for Combining Preferences.
Multistrategy Learning for Information Extraction.
Using a Permutation Test for Attribute Selection in Decision Trees.
Generating Accurate Rule Sets Without Global Optimization.
Relational Reinforcement Learning.
A Process-Oriented Heuristic for Model Selection.
The MAXQ Method for Hierarchical Reinforcement Learning.
Bayesian Classifiers Are Large Margin Hyperplanes in a Hilbert Space.
Finite-Time Regret Bounds for the Multiarmed Bandit Problem.
Refining Initial Points for K-Means Clustering.
Feature Selection via Concave Minimization and Support Vector Machines.
Learning Sorting and Decision Trees with POMDPs.
A Supra-Classifier Architecture for Scalable Knowledge Reuse.
Top-Down Induction of Clustering Trees.
Learning Collaborative Information Filters.
Combining Nearest Neighbor Classifiers Through Multiple Feature Subsets.
KnightCap: A Chess Programm That Learns by Combining TD(lambda) with Game-Tree Search.
An Experimental Evaluation of Coevolutive Concept Learning.
Genetic Programming and Deductive-Inductive Learning: A Multi-Strategy Approach.
Query Learning Strategies Using Boosting and Bagging.