icml 2002 论文列表Machine Learning, Proceedings of the Nineteenth International Conference (ICML 2002), University of New South Wales, Sydney, Australia, July 8-12, 2002. |
Statistical Behavior and Consistency of Support Vector Machines, Boosting, and Beyond.
Content-Based Image Retrieval Using Multiple-Instance Learning.
Representational Upper Bounds of Bayesian Networks.
Non-Disjoint Discretization for Naive-Bayes Classifiers.
Mining Both Positive and Negative Association Rules.
Modeling for Optimal Probability Prediction.
Issues in Classifier Evaluation using Optimal Cost Curves.
Sparse Bayesian Learning for Regression and Classification using Markov Chain Monte Carlo.
Refining the Wrapper Approach - Smoothed Error Estimates for Feature Selection.
Finding an Optimal Gain-Ratio Subset-Split Test for a Set-Valued Attribute in Decision Tree Induction.
Qualitative reverse engineering.
Markov Chain Monte Carlo Sampling using Direct Search Optimization.
Randomized Variable Elimination.
Learning to Fly by Controlling Dynamic Instabilities.
Discriminative Feature Selection via Multiclass Variable Memory Markov Model.
Separating Skills from Preference: Using Learning to Program by Reward.
Model-based Hierarchical Average-reward Reinforcement Learning.
How to Make Stacking Better and Faster While Also Taking Care of an Unknown Weakness.
Modeling Auction Price Uncertainty Using Boosting-based Conditional Density Estimation.
Incorporating Prior Knowledge into Boosting.
Syllables and other String Kernel Extensions.
Using Abstract Models of Behaviours to Automatically Generate Reinforcement Learning Hierarchies.
Using Unlabelled Data for Text Classification through Addition of Cluster Parameters.
PolicyBlocks: An Algorithm for Creating Useful Macro-Actions in Reinforcement Learning.
Learning from Scarce Experience.
On the Existence of Fixed Points for Q-Learning and Sarsa in Partially Observable Domains.
A Boosted Maximum Entropy Model for Learning Text Chunking.
Learning Spatial and Temporal Correlation for Navigation in a 2-Dimensional Continuous World.
MMIHMM: Maximum Mutual Information Hidden Markov Models.
Learning k-Reversible Context-Free Grammars from Positive Structural Examples.
Stock Trading System Using Reinforcement Learning with Cooperative Agents.
Adaptive View Validation: A First Step Towards Automatic View Detection.
Active + Semi-supervised Learning = Robust Multi-View Learning.
Learning word normalization using word suffix and context from unlabeled data.
Towards "Large Margin" Speech Recognizers by Boosting and Discriminative Training.
A Necessary Condition of Convergence for Reinforcement Learning with Function Approximation.
Investigating the Maximum Likelihood Alternative to TD(lambda).
Feature Selection with Selective Sampling.
Partially Supervised Classification of Text Documents.
The Perceptron Algorithm with Uneven Margins.
Learning to Share Distributed Probabilistic Beliefs.
Cranking: Combining Rankings Using Conditional Probability Models on Permutations.
Reinforcement Learning and Shaping: Encouraging Intended Behaviors.
Inducing Process Models from Continuous Data.
Competitive Analysis of the Explore/Exploit Tradeoff.
Combining Trainig Set and Test Set Bounds.
Learning the Kernel Matrix with Semi-Definite Programming.
Diffusion Kernels on Graphs and Other Discrete Input Spaces.
From Instance-level Constraints to Space-Level Constraints: Making the Most of Prior Knowledge in Data Clustering.
A Fast Dual Algorithm for Kernel Logistic Regression.
Kernels for Semi-Structured Data.
Interpreting and Extending Classical Agglomerative Clustering Algorithms using a Model-Based approach.
An Alternate Objective Function for Markovian Fields.
Approximately Optimal Approximate Reinforcement Learning.
Linkage and Autocorrelation Cause Feature Selection Bias in Relational Learning.
Classification Value Grouping.
Discovering Hierarchy in Reinforcement Learning with HEXQ.
Algorithm-Directed Exploration for Model-Based Reinforcement Learning in Factored MDPs.
Coordinated Reinforcement Learning.
Graph-Based Relational Concept Learning.
A Unified Decomposition of Ensemble Loss for Predicting Ensemble Performance.
Sufficient Dimensionality Reduction - A novel Analysis Method.
Hierarchically Optimal Average Reward Reinforcement Learning.
Combining Labeled and Unlabeled Data for MultiClass Text Categorization.
Multi-Instance Kernels.
On generalization bounds, projection profile, and margin distribution.
Descriptive Induction through Subgroup Discovery: A Case Study in a Medical Domain.
An Analysis of Functional Trees.
Univariate Polynomial Inference by Monte Carlo Message Length Approximation.
Learning Decision Trees Using the Area Under the ROC Curve.
Fast Minimum Training Error Discretization.
Is Combining Classifiers Better than Selecting the Best One.
Integrating Experimentation and Guidance in Relational Reinforcement Learning.
Action Refinement in Reinforcement Learning by Probability Smoothing.
Anytime Interval-Valued Outputs for Kernel Machines: Fast Support Vector Machine Classification via Distance Geometry.
Exact model averaging with naive Bayesian classifiers.
IEMS - The Intelligent Email Sorter.
Learning Decision Rules by Randomized Iterative Local Search.
A New Statistical Approach to Personal Name Extraction.
Transformation-Based Regression.
An epsilon-Optimal Grid-Based Algorithm for Partially Observable Markov Decision Processes.
Exploiting Relations Among Concepts to Acquire Weakly Labeled Training Data.
Constraint-based Learning of Long Relational Concepts.
Semi-supervised Clustering by Seeding.
Pruning Improves Heuristic Search for Cost-Sensitive Learning.
Feature Subset Selection and Inductive Logic Programming.
Scalable Internal-State Policy-Gradient Methods for POMDPs.