WhatToSee

Index of ICML 2003

• A Faster Iterative Scaling Algorithm For Conditional Exponential Model
• A Kernel Between Sets of Vectors
• AWESOME: A General Multiagent Learning Algorithm that Converges in Self-Play and Learns a Best Resp onse Against Stationary Opp onents
• Action Elimination and Stopping Conditions for Reinforcement Learning
• Adaptive Feature-Space Conformal Transformation for Imbalanced-Data Learning
• Adaptive Overrelaxed Bound Optimization Methods
• An Analysis of Rule Evaluation Metrics
• An Evaluation on Feature Selection for Text Clustering
• BL-WoLF: A Framework For Loss-Bounded Learnability In Zero-Sum Games
• Characteristics of Long-term Learning in Soar and its Application to the Utility Problem
• Choosing b etween two learning algorithms based on calibrated tests
• Correlated-Q Learning
• Cross-Entropy Directed Emb edding of Network Data
• DISTILL: Learning Domain-Specific Planners by Example
• Decision-tree Induction from Time-series Data Based on a Standard-example Split Test
• Eliminating Class Noise in Large Datasets
• Exploration in Metric State Spaces
• Feature Selection for High-Dimensional Data: A Fast Correlation-Based Filter Solution
• Finding Underlying Connections: A Fast Graph-Based Method for Link Analysis and Collaboration Queries
• Hierarchical Policy Gradient Algorithms
• Identifying Predictive Structures in Relational Data Using Multiple Instance Learning
• Improving accuracy and cost of two-class and multi-class probabilistic classifiers using ROC curves
• Justification-based Multiagent Learning
• Kernel PLS-SVC for Linear and Nonlinear Classification
• Learning Decision Tree Classifiers from Attribute Value Taxonomies and Partially Specified Data
• Learning Logic Programs for Layout Analysis Correction
• Learning Metrics via Discriminant Kernels and Multidimensional Scaling: Toward Exp ected Euclidean Representation
• Learning Mixture Models with the Latent Maximum Entropy Principle
• Learning with Positive and Unlabeled Examples Using Weighted Logistic Regression
• Machine Learning using Hyp erkernels
• Margin Distribution and Learning Algorithms
• Modified Logistic Regression: An Approximation to SVM and Its Applications in Large-Scale Text Categorization
• Online Choice of Active Learning Algorithms
• Online Convex Programming and Generalized Infinitesimal Gradient Ascent
• Online Feature Selection using Grafting
• Optimal Reinsertion: A new search operator for accelerated and more accurate Bayesian network structure learning
• Optimization with EM and Expectation-Conjugate-Gradient
• Optimizing Classifier Performance via an Approximation to the Wilcoxon-Mann-Whitney Statistic
• Planning in the Presence of Cost Functions Controlled by an Adversary
• Principled Metho ds for Advising Reinforcement Learning Agents
• Probabilistic Classifiers and the Concepts they Recognize
• Q-Decomposition for Reinforcement Learning Agents
• Random Pro jection for High Dimensional Data Clustering: A Cluster Ensemble Approach
• Relativized Options: Cho osing the Right Transformation
• Representational Issues in Meta-Learning
• Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions
• Semi-Supervised Learning of Mixture Models
• SimpleSVM
• TD(0) Converges Provably Faster than the Residual Gradient Algorithm
• Tackling the Po or Assumptions of Naive Bayes Text Classifiers
• Testing Exchangeability On-Line
• Text Bundling: Statistics-Based Data Reduction
• The Cross Entropy metho d for Fast Policy Search
• The Geometry of ROC Space: Understanding Machine Learning Metrics through ROC Isometrics
• The Influence of Reward on the Speed of Reinforcement Learning: An Analysis of Shaping
• The Pre-Image Problem in Kernel Metho ds
• The Set Covering Machine with Data-Dependent Half-Spaces
• The Significance of Temporal-Difference Learning in Self-Play Training TD-rummy versus EVO-rummy
• The Use of the Ambiguity Decomp osition in Neural Network Ensemble Learning Metho ds
• Tractable Bayesian Learning of Tree Augmented Naive Bayes Mo dels
• Transductive Learning via Spectral Graph Partitioning
• Unsupervised Learning with Permuted Data
• Using Linear-threshold Algorithms to Combine Multi-class Sub-experts
• Using the Triangle Inequality to Accelerate -Means
• Visual Learning by Evolutionary Feature Synthesis
• Weighted Low-Rank Approximations
• Weighted Order Statistic Classifiers with Large Rank-Order Margin