WhatToSee

Index of ICML 2004

• A Comparative Study on Metho ds for Reducing Myopia of Hill-Climbing Search in Multirelational Learning
• A Fast Iterative Algorithm for Fisher Discriminant using Heterogeneous Kernels
• A Graphical Mo del for Protein Secondary Structure Prediction
• A MFoM Learning Approach to Robust Multiclass Multi-Label Text Categorization
• A Monte Carlo Analysis of Ensemble Classification
• A Pitfall and Solution in Multi-Class Feature Selection for Text Classification
• A Spatio-temp oral Extension to Isomap Nonlinear Dimension Reduction
• Active Learning Using Pre-clustering
• Active Learning of Lab el Ranking Functions
• Adaptive Cognitive Orthotics: Combining Reinforcement Learning and Constraint-Based Temp oral Reasoning
• Apprenticeship Learning via Inverse Reinforcement Learning
• Approximate Inference by Markov Chains on Union Spaces
• Automated Hierarchical Mixtures of Probabilistic Principal Comp onent Analyzers
• Bayesian Haplotyp e Inference via the Dirichlet Pro cess
• Bayesian Inference for Transductive Learning of Kernel Matrix Using the Tanner-Wong Data Augmentation Algorithm
• Bellman go es Relational
• Boosting Margin Based Distance Functions for Clustering
• Co-EM Supp ort Vector Learning
• Communication complexity as a lower b ound for learning in games
• Convergence of Synchronous Reinforcement Learning with Linear Function Approximation
• Delegating Classifiers
• Dynamic Conditional Random Fields: Factorized Probabilistic Models for Labeling and Segmenting Sequence Data
• Entropy-Based Criterion in Categorical Clustering
• Estimating Replicability of Classifier Learning Exp eriments
• Efficient Hierarchical MCMC for Policy Search
• Feature Subset Selection for Learning Preferences: A Case Study
• Feature selection, L1 vs. L2 regularization, and rotational invariance
• Gaussian Pro cess Classification for Segmenting and Annotating Sequences
• Generalized Low Rank Approximations of Matrices
• Gradient LASSO for feature selection
• Hyperplane Margin Classifiers on the Multinomial Manifold
• Integrating Constraints and Metric Learning in Semi-Supervised Clustering
• Interpolation-based Q-learning
• Kernel-based Discriminative Learning Algorithms for Lab eling Sequences, Trees, and Graphs
• Learning Bayesian Network Classifiers by Maximizing Conditional Likeliho o d
• Learning First-order Rules from Data with Multiple Parts: Applications on Mining Chemical Comp ound Data
• Learning Low Dimensional Predictive Representations
• Learning a Kernel Matrix for Nonlinear Dimensionality Reduction
• Learning to Cluster using Lo cal Neighb orho o d Structure
• Learning to Learn with the Informative Vector Machine
• Learning to Track 3D Human Motion from Silhouettes
• Learning with Non-Positive Kernels
• Leveraging the Margin More Carefully
• Linearized Cluster Assignment via Sp ectral Ordering
• Locally Linear Metric Adaptation for Semi-Sup ervised Clustering
• Lookahead-based Algorithms for Anytime Induction of Decision Trees
• Margin Based Feature Selection - Theory and Algorithms
• Multiple Kernel Learning, Conic Duality, and the SMO Algorithm
• Nonparametric Classification with Polynomial MPMC Cascades
• Online Learning of Conditionally I.I.D. Data
• Online and Batch Learning of Pseudo-Metrics
• Optimising Area Under the ROC Curve Using Gradient Descent
• Predictive Automatic Relevance Determination by Exp ectation Propagation
• Redundant Feature Elimination for Multi-Class Problems
• SVM-Based Generalized Multiple-Instance Learning via Approximate Box Counting
• Sequential Skewing: An Improved Skewing Algorithm
• Support Vector Machine Learning for Interdep endent and Structured Output Spaces
• Testing the Significance of Attribute Interactions
• Text Categorization with Many Redundant Features: Using Aggressive Feature Selection to Make SVMs Comp etitive with C4.5
• Towards Tight Bounds for Rule Learning
• Unifying Collab orative and Content-Based Filtering