WhatToSee

Index of ICML 2005

• 2D Conditional Random Fields for Web Information Extraction
• A Brain Computer Interface with Online Feedback based on Magneto encephalography
• A Graphical Mo del for Chord Progressions Emb edded in a Psychoacoustic Space
• A Martingale Framework for Concept Change Detection in Time-Varying Data Streams
• A Mo del for Handling Approximate, Noisy or Incomplete Lab eling in Text Classification
• A New Mallows Distance Based Metric For Comparing Clusterings
• A Practical Generalization of Fourier-based Learning
• A Smo othed Bo osting Algorithm Using Probabilistic Output Co des
• A Supp ort Vector Metho d for Multivariate Performance Measures
• A Theoretical Analysis of Mo del-Based Interval Estimation
• Action Respecting Embedding
• Active Learning for Hidden Markov Models: Objective Functions and Algorithms
• Active Learning for Sampling in Time-Series Exp eriments With Application to Gene Expression Analysis
• Adapting Two-Class Supp ort Vector Classification Metho ds to Many Class Problems
• An Efficient Metho d for Simplifying Supp ort Vector Machines
• Augmenting Naive Bayes for Ranking
• Bayesian Hierarchical Clustering
• Clustering Through Ranking On Manifolds
• Coarticulation: An Approach for Generating Concurrent Plans in Markov Decision Pro cesses
• Compact approximations to Bayesian predictive distributions
• Computational Asp ects of Bayesian Partition Mo dels
• Core Vector Regression for Very Large Regression Problems
• Discriminative versus Generative Parameter and Structure Learning of Bayesian Network Classifiers
• Ensembles of Biased Classifiers
• Error Limiting Reductions Between Classification Tasks
• Evaluating Machine Learning for Information Extraction
• Expectation Maximization Algorithms for Conditional Likeliho o ds
• Experimental Comparison b etween Bagging and Monte Carlo Ensemble Classification
• Explanation-Augmented SVM: an Approach to Incorporating Domain Knowledge into SVM Learning
• Exploiting Syntactic, Semantic and Lexical Regularities in Language Modeling via Directed Markov Random Fields
• Exploration and Apprenticeship Learning in Reinforcement Learning
• Fast Inference and Learning in Large-State-Space HMMs
• Fast Maximum Margin Matrix Factorization for Collaborative Prediction
• Finite Time Bounds for Sampling Based Fitted Value Iteration
• Generalized LARS as an Effective Feature Selection To ol for Text Classification With SVMs
• Healing the Relevance Vector Machine through Augmentation
• Hedged learning: Regret-minimization with learning exp erts
• Hierarchic Bayesian Mo dels for Kernel Learning
• Hierarchical Dirichlet Mo del for Do cument Classification
• High Sp eed Obstacle Avoidance using Mono cular Vision and Reinforcement Learning
• Identifying Useful Subgoals in Reinforcement Learning by Lo cal Graph Partitioning
• Incomplete-Data Classification using Logistic Regression
• Interactive Learning of Mappings from Visual Percepts to Actions
• Intrinsic Dimensionality Estimation of Submanifolds in Rd
• Large Margin Non-Linear Emb edding
• Large Scale Genomic Sequence SVM Classifiers
• Learn to Weight Terms in Information Retrieval Using Category Information
• Learning Discontinuities with Pro ducts-of-Sigmoids for Switching b etween Lo cal Mo dels
• Learning Gaussian Pro cesses from Multiple Tasks
• Learning Hierarchical Multi-Category Text Classification Mo dels
• Learning Predictive Representations from a History
• Learning Predictive State Representations in Dynamical Systems Without Reset
• Learning Strategies for Story Comprehension: A Reinforcement Learning Approach
• Learning Structured Prediction Mo dels: A Large Margin Approach
• Learning as Search Optimization: Approximate Large Margin Metho ds for Structured Prediction
• Learning the Structure of Markov Logic Networks
• Learning to Comp ete, Compromise, and Co op erate in Rep eated General-Sum Games
• Linear Asymmetric Classifier for Cascade Detectors
• Logistic Regression with an Auxiliary Data Source
• Multi-Instance Tree Learning
• Multi-class protein fold recognition using adaptive codes
• Multimodal Oriented Discriminant Analysis
• Naive Bayes Models for Probability Estimation
• Near-Optimal Sensor Placements in Gaussian Processes
• New Approaches to Supp ort Vector Ordinal Regression
• New D-Separation Identification Results for Learning Continuous Latent Variable Mo dels
• Non-Negative Tensor Factorization with Applications to Statistics and Computer Vision
• Object Corresp ondence as a Machine Learning Problem
• Online Feature Selection for Pixel Classification
• Online Learning over Graphs
• Optimal Assignment Kernels For Attributed Molecular Graphs
• PAC-Bayes Risk Bounds for Sample-Compressed Gibbs Classifiers
• Predicting Good Probabilities With Supervised Learning
• Predicting Probability Distributions for Surf Height Using an Ensemble of Mixture Density Networks
• Predicting Protein Folds with Structural Rep eats Using a Chain Graph Mo del
• Predicting Relative Performance of Classifiers from Samples
• Predictive low-rank decomp osition for kernel metho ds
• Propagating Distributions on a Hyp ergraph by Dual Information Regularization
• Proto-Value Functions: Developmental Reinforcement Learning
• Q-Learning of Sequential Attention for Visual Ob ject Recognition from Informative Lo cal Descriptors
• ROC Confidence Bands: An Empirical Evaluation
• Recycling Data for Multi-Agent Learning
• Reducing Overfitting in Pro cess Mo del Induction
• Reinforcement learning with Gaussian pro cesses
• Relating Reinforcement Learning Performance to Classification Performance
• Robust One-Class Clustering using Hybrid Global and Lo cal Search
• Semi-supervised Graph Clustering: A Kernel Approach
• Statistical and Computational Analysis of Lo cality Preserving Pro jection
• Supervised Clustering with Supp ort Vector Machines
• TD() Networks: Temporal-Difference Networks with Eligibility Traces
• Tempering for Bayesian C&RT
• The cross entropy metho d for classification
• Unsupervised Evidence Integration
• Using Additive Exp ert Ensembles to Cop e with Concept Drift
• Variational Bayesian Image Modelling
• Weighted Decomp osition Kernels
• Why Skewing Works: Learning Difficult Bo olean Functions with Greedy Tree Learners