WhatToSee

Index of ICML 2006

• A Continuation Metho d for Semi-Sup ervised SVMs
• A DC-Programming Algorithm for Kernel Selection
• A Duality View of Sp ectral Metho ds for Dimensionality Reduction
• A Graphical Mo del for Predicting Protein Molecular Function
• A Note on Mixtures of Exp erts for Multiclass Resp onses: Approximation Rate and Consistent Bayesian Inference
• A Regularization Framework for Multiple-Instance Learning
• A Statistical Approach to Rule Learning
• Accelerated Training of Conditional Random Fields with Sto chastic Gradient Metho ds
• Active Learning via Transductive Exp erimental Design
• Active Sampling for Detecting Irrelevant Features
• Agnostic Active Learning
• Algorithms for Portfolio Management based on the Newton Metho d
• An Analysis of Graph Cut Size for Transductive Learning
• An Analytic Solution to Discrete Bayesian Reinforcement Learning
• An Empirical Comparison of Sup ervised Learning Algorithms
• An Investigation of Computational and Informational Limits in Gaussian Mixture Clustering
• Autonomous Shaping: Knowledge Transfer in Reinforcement Learning
• Batch Mo de Active Learning and Its Application to Medical Image Classification
• Bayesian Learning of Measurement and Structural Mo dels
• Bayesian Multi-Population Haplotyp e Inference via a Hierarchical Dirichlet Pro cess Mixture
• Bayesian Regression with Input Noise for High Dimensional Data
• Block-Quantized Kernel Matrix for Fast Sp ectral Emb edding
• CN=CPCN
• Classifying EEG for Brain-Computer Interfaces: Learning Optimal Filters for Dynamical System Features
• Clustering Documents with an Exponential-Family Approximation of the Dirichlet Compound Multinomial Distribution
• Clustering Graphs by Weighted Substructure Mining
• Collaborative Prediction Using Ensembles of Maximum Margin Matrix Factorizations
• Combined Central and Subspace Clustering for Computer Vision Applications
• Combining Discriminative Features to Infer Complex Tra jectories
• Constructing Informative Priors using Transfer Learning
• Convex Optimization Techniques for Fitting Sparse Gaussian Graphical Mo dels
• Cost-Sensitive Learning with Conditional Markov Networks
• Data Asso ciation for Topic Intensity Tracking
• Dealing with Non-Stationary Environments using Context Detection
• Deterministic Annealing for Semi-sup ervised Kernel Machines
• Discriminative Cluster Analysis
• Discriminative Unsup ervised Learning of Structured Predictors
• Dynamic Topic Models
• Estimating Relatedness via Data Compression
• Experience-Efficient Learning in Asso ciative Bandit Problems
• Efficient Co-Regularised Least Squares Regression
• Efficient Lazy Elimination for Averaged One-Dep endence Estimators
• Efficient Learning of Naive Bayes Classifiers under Class-Conditional Classification Noise
• Fast Direct Policy Evaluation using Multiscale Analysis of Markov Diffusion Pro cesses
• Fast Particle Smo othing: If I Had a Million Particles
• Fast Time Series Classification Using Numerosity Reduction
• Fast Transp ose Metho ds for Kernel Learning on Sparse Data
• Fast and Space Efficient String Kernels using Suffix Arrays
• Feature Subset Selection Bias for Classification Learning
• Feature Value Acquisition in Testing: A Sequential Batch Test Algorithm
• Full Bayesian Network Classifiers
• Generalized Sp ectral Bounds for Sparse LDA
• Graph Mo del Selection using Maximum Likeliho o d
• Hidden Process Models
• Hierarchical Classification: Combining Bayes with SVM
• Inference with the Universum
• Iterative RELIEF for Feature Weighting
• Kernel Information Emb eddings
• Kernel Predictive Linear Gaussian Models for Nonlinear Stochastic Dynamical Systems
• Label Propagation Through Linear Neighb orho o ds
• Learning Algorithms for Online Principal-Agent Problems (and Selling Goods Online)
• Learning High-Order MRF Priors of Color Images
• Learning Low-Rank Kernel Matrices
• Learning Predictive State Representations Using Non-Blind Policies
• Learning User Preferences for Sets of Ob jects
• Learning a Kernel Function for Classification with Small Training Samples
• Learning to Imp ersonate
• Local Distance Preservation in the GP-LVM through Back Constraints
• Local Fisher Discriminant Analysis for Sup ervised Dimensionality Reduction
• Locally Adaptive Classification Piloted by Uncertainty
• Looping Suffix Tree-Based Inference of Partially Observable Hidden State
• MISSL: Multiple-Instance Semi-Sup ervised Learning
• Multiclass Bo osting with Repartitioning
• Multiclass Reduced-Set Supp ort Vector Machines
• Nightmare at Test Time: Robust Learning by Feature Deletion
• Nonstationary Kernel Combination
• Null Space versus Orthogonal Linear Discriminant Analysis
• On Bayesian Bounds
• On a Theory of Learning with Similarity Functions
• Online Deco ding of Markov Mo dels under Latency Constraints
• Online Multiclass Learning by Interclass Hypothesis Sharing
• Optimal Kernel Selection in Kernel Fisher Discriminant Analysis
• PAC Mo del-Free Reinforcement Learning
• Pachinko Allo cation: DAG-Structured Mixture Mo dels of Topic Correlations
• Pareto Optimal Linear Classification
• Permutation Invariant SVMs
• Practical Solutions to the Problem of Diagonal Dominance in Kernel Do cument Clustering
• Predictive Linear-Gaussian Models of Controlled Stochastic Dynamical Systems
• Predictive Search Distributions
• Predictive State Representations with Options
• Probabilistic Inference for Solving Discrete and Continuous State Markov Decision Pro cesses
• Pruning in Ordered Bagging Ensembles
• Quadratic Programming Relaxations for Metric Labeling and Markov Random Field MAP Estimation
• Qualitative Reinforcement Learning
• R1 -PCA: Rotational Invariant L1 -norm Principal Comp onent Analysis for Robust Subspace Factorization
• Ranking Individuals by Group Comparisons
• Ranking on Graph Data
• Regression with the Optimised Combination Technique
• Reinforcement Learning for Optimized Trade Execution
• Relational Temp oral Difference Learning
• Semi-Supervised Nonlinear Dimensionality Reduction
• Sequential Up date of ADtrees
• Simpler Knowledge-based Support Vector Machines
• Spectral Clustering for Multi-type Relational Data
• The Rate Adapting Poisson Model for Information Retrieval and Object Recognition
• The Relationship Between Precision-Recall and ROC Curves
• The Supp ort Vector Decomp osition Machine
• The Uniqueness of a Go o d Optimum for K-Means
• Topic Mo deling: Beyond Bag-of-Words
• Totally Corrective Bo osting Algorithms that Maximize the Margin
• Trading Convexity for Scalability
• Two-Dimensional Solution Path for Supp ort Vector Regression
• Using Inaccurate Mo dels in Reinforcement Learning