WhatToSee

Index of JMLR Vols 1-8

• A Bayes Optimal Approach for Partitioning the Values of Categorical Attributes
• A Bayesian Model for Supervised Clustering with the Dirichlet Process Prior
• A Classification Framework for Anomaly Detection
• A Direct Metho d for Building Sparse Kernel Learning Algorithms
• A Divisive Information-Theoretic Feature Clustering Algorithm for Text Classification
• A Family of Additive Online Algorithms for Category Ranking
• A Fast Algorithm for Joint Diagonalization with Non-orthogonal Transformations and its Application to Blind Source Separation
• A Framework for Learning Predictive Structures from Multiple Tasks and Unlab eled Data
• A Generalization Error for Q-Learning
• A Generalized Kernel Approach to Dissimilarity-based Classification
• A Generalized Kernel Approach to Dissimilarity-based Classification
• A Graphical Representation of Equivalence Classes of AMP Chain Graphs
• A Hierarchy of Support Vector Machines for Pattern Detection
• A Linear Non-Gaussian Acyclic Model for Causal Discovery
• A Modified Finite Newton Method for Fast Solution of Large Scale Linear SVMs
• A Neural Probabilistic Language Model
• A New Approximate Maximal Margin Classification Algorithm
• A Probabilistic Analysis of EM for Mixtures of Separated, Spherical Gaussians
• A Robust Minimax Approach to Classification
• A Robust Minimax Approach to Classification
• A Robust Procedure For Gaussian Graphical Model Search From Microarray Data With p Larger Than n
• A Scoring Function for Learning Bayesian Networks based on Mutual Information and Conditional Independence Tests
• A Simulation-Based Algorithm for Ergodic Control of Markov Chains Conditioned on Rare Events
• A Stochastic Algorithm for Feature Selection in Pattern Recognition
• A Unified Continuous Optimization Framework for Center-Based Clustering Methods
• A Unifying View of Sparse Approximate Gaussian Process Regression
• A Very Fast Learning Method for Neural Networks Based on Sensitivity Analysis
• Accurate Error Bounds for the Eigenvalues of the Kernel Matrix
• Action Elimination and Stopping Conditions for the Multi-Armed Bandit and Reinforcement Learning Problems
• Active Coevolutionary Learning of Deterministic Finite Automata
• Active Learning in Approximately Linear Regression Based on Conditional Expectation of Generalization Error
• Active Learning to Recognize Multiple Types of Plankton
• Active Learning with Feedback on Both Features and Instances
• Adaptive Online Prediction by Following the Perturbed Leader
• Adaptive Prototype Learning Algorithms: Theoretical and Experimental Studies
• Algorithmic Luckiness
• Algorithmic Luckiness
• Algorithmic Stability and Meta-Learning
• An Efficient Implementation of an Active Set Method for SVMs
• An Extensive Empirical Study of Feature Selection Metrics for Text Classification
• An Extensive Empirical Study of Feature Selection Metrics for Text Classification
• An Extensive Empirical Study of Feature Selection Metrics for Text Classification
• An Introduction to Variable and Feature Selection
• An MDP-Based Recommender System
• Analysis of Variance of Cross-Validation Estimators of the Generalization Error
• Assessing Approximate Inference for Binary Gaussian Process Classification
• Bayes Point Machines
• Bayesian Network Learning with Parameter Constraints
• Benefitting from the Variables that Variable Selection Discards
• Bias-Variance Analysis of Support Vector Machines for the Development of SVM-Based Ensemble Methods
• Boosted Classification Trees and Class Probability/Quantile Estimation
• Boosting as a Regularized Path to a Maximum Margin Classifier
• Bounds for Linear Multi-Task Learning
• Bounds for the Loss in Probability of Correct Classification Under Model Based Approximation
• Building Blocks for Variational Bayesian Learning of Latent Variable Models
• Building Support Vector Machines with Reduced Classifier Complexity
• Causal Graph Based Decomposition of Factored MDPs
• Change Point Problems in Linear Dynamical Systems
• Characterization of a Family of Algorithms for Generalized Discriminant Analysis on Undersampled Problems
• Classes of Kernels for Machine Learning: A Statistics Persp ective
• Cluster Ensembles ­ A Knowledge Reuse Framework for Combining Multiple Partitions
• Cluster Ensembles ­ A Knowledge Reuse Framework for Combining Multiple Partitions
• Clustering on the Unit Hypersphere using von Mises-Fisher Distributions
• Clustering with Bregman Divergences
• Collaborative Multiagent Reinforcement Learning by Payoff Propagation
• Combining Information Extraction Systems Using Voting and Stacked Generalization
• Combining PAC-Bayesian and Generic Chaining Bounds
• Comments on the "Core Vector Machines: Fast SVM Training on Very Large Data Sets"
• Computational and Theoretical Analysis of Null Space and Orthogonal Linear Discriminant Analysis
• Concave Learners for Rankboost
• Concentration Bounds for Unigram Language Models
• Considering Cost Asymmetry in Learning Classifiers
• Consistency and Convergence Rates of One-Class SVMs and Related Algorithms
• Consistency of Multiclass Empirical Risk Minimization Methods Based on Convex Loss
• Consistent Feature Selection for Pattern Recognition in Polynomial Time
• Convergence Theorems for Generalized Alternating Minimization Procedures
• Core Vector Machines: Fast SVM Training on Very Large Data Sets
• Coupled Clustering: A Method for Detecting Structural Correspondence
• Covering Numb er Bounds of Certain Regularized Linear Function Classes
• Covering Numb er Bounds of Certain Regularized Linear Function Classes
• Data-dep endent margin-based generalization b ounds for classification
• Data-dep endent margin-based generalization b ounds for classification
• Denoising Source Separation
• Dep endency Networks for Inference, Collab orative Filtering, and Data Visualization
• Diffusion Kernels on Statistical Manifolds
• Dimension Reduction in Text Classification with Support Vector Machines
• Dimensionality Reduction via Sparse Support Vector Machines
• Distance Patterns in Structural Similarity
• Distances between Data Sets Based on Summary Statistics
• Distributional Word Clusters vs. Words for Text Categorization
• Dynamic Conditional Random Fields: Factorized Probabilistic Models for Labeling and Segmenting Sequence Data
• Dynamics and Generalization Ability of LVQ Algorithms
• Efficient Algorithms for Decision Tree Cross-validation
• Efficient Algorithms for Universal Portfolios
• Efficient Computation of Gapped Substring Kernels on Large Alphabets
• Efficient Feature Selection via Analysis of Relevance and Redundancy
• Efficient Learning of Label Ranking by Soft Projections onto Polyhedra
• Efficient Margin Maximizing with Boosting
• Efficient SVM Training Using Low-Rank Kernel Representations
• Ensemble Pruning Via Semi-definite Programming
• Estimating Functions for Blind Separation When Sources Have Variance Dependencies
• Estimating High-Dimensional Directed Acyclic Graphs with the PC-Algorithm
• Estimating the "Wrong" Graphical Model: Benefits in the Computation-Limited Setting
• Estimation of Gradients and Coordinate Covariation in Classification
• Estimation of Non-Normalized Statistical Mo dels by Score Matching
• Evolutionary Function Approximation for Reinforcement Learning
• Exact 1-Norm Support Vector Machines via Unconstrained Convex Differentiable Minimization
• Exact Simplification of Support Vector Solutions
• Exp ectation Consistent Approximate Inference
• Expectation Correction for Smoothed Inference in Switching Linear Dynamical Systems
• Extensions to Metric-Based Model Selection
• Fast Binary Feature Selection with Conditional Mutual Information
• Fast Kernel Classifiers with Online and Active Learning
• Fast SDP Relaxations of Graph Cut Clustering, Transduction,and Other Combinatorial Problems
• Fast String Kernels using Inexact Matching for Protein Sequences
• Feature Discovery in Non-Metric Pairwise Data
• Feature Extraction by Non-Parametric Mutual Information Maximization
• Feature Selection for Unsupervised Learning
• Feature Selection for Unsupervised and Supervised Inference: The Emergence of Sparsity in a Weight-Based Approach
• Finding the Most Interesting Patterns in a Database Quickly by Using Sequential Sampling
• Frames, Reproducing Kernels, Regularization and Learning
• Gaussian Processes for Ordinal Regression
• General Polynomial Time Decomposition Algorithms
• Generalization Bounds and Complexities Based on Sparsity and Clustering for Convex Combinations of Functions from Random Classes
• Generalization Bounds for the Area Under the ROC Curve
• Generalized Bradley-Terry Mo dels and Multi-Class Probability Estimates
• Geometric Variance Reduction in Markov Chains: Application to Value Function and Gradient Estimation
• Gini Support Vector Machine: Quadratic Entropy Based Robust Multi-Class Probability Regression
• Grafting: Fast, Incremental Feature Selection by Gradient Descent in Function Space
• Graph-Based Hierarchical Conceptual Clustering
• Graph-Based Hierarchical Conceptual Clustering
• Hierarchical Latent Class Models for Cluster Analysis
• Image Categorization by Learning and Reasoning with Regions
• In Search of Non-Gaussian Components of a High-Dimensional Distribution
• Incremental Algorithms for Hierarchical Classification
• Incremental Support Vector Learning: Analysis, Implementation and Applications
• Inductive Synthesis of Functional Programs: An Explanation Based Generalization Approach
• Infinite- Limits For Tikhonov Regularization
• Infinitely Imbalanced Logistic Regression
• Information Bottleneck for Gaussian Variables
• Inner Product Spaces for Bayesian Networks
• Integrating Na¨ve Bayes and F O I L i
• Intro duction to Sp ecial Issue on Machine Learning Approaches to Shallow Parsing
• Kernel Indep endent Comp onent Analysis
• Kernel Indep endent Comp onent Analysis
• Kernel Methods for Measuring Independence
• Kernel Methods for Relation Extraction
• Kernel Partial Least Squares Regression in Repro ducing Kernel Hilb ert Space
• Kernel-Based Learning of Hierarchical Multilabel Classification Models
• Kernels on Prolog Proof Trees: Statistical Learning in the ILP Setting
• Knowledge-Based Kernel Approximation
• Lagrangian Supp ort Vector Machines
• Large Margin Methods for Structured and Interdependent Output Variables
• Large Scale Multiple Kernel Learning
• Large Scale Transductive SVMs
• Latent Dirichlet Allocation
• Latent Dirichlet Allocation
• Learnability of Gaussians with Flexible Variances
• Learning Coordinate Covariances via Gradients
• Learning Equivalence Classes of Bayesian-Network Structures
• Learning Equivalence Classes of Bayesian-Network Structures
• Learning Equivariant Functions with Matrix Valued Kernels
• Learning Factor Graphs in Polynomial Time and Sample Complexity
• Learning Hidden Variable Networks: The Information Bottleneck Approach
• Learning Horn Expressions with L O G A N - H
• Learning Image Components for Object Recognition
• Learning Minimum Volume Sets
• Learning Module Networks
• Learning Monotone DNF from a Teacher that Almost Do es Not Answer Membership Queries
• Learning Monotone DNF from a Teacher that Almost Do es Not Answer Membership Queries
• Learning Multiple Tasks with Kernel Methods
• Learning Parts-Based Representations of Data
• Learning Precise Timing with LSTM Recurrent Networks
• Learning Precise Timing with LSTM Recurrent Networks
• Learning Probabilistic Models of Link Structure
• Learning Recursive Control Programs from Problem Solving
• Learning Rules and Their Exceptions
• Learning Sparse Representations by Non-Negative Matrix Factorization and Sequential Cone Programming
• Learning Spectral Clustering, With Application To Speech Separation
• Learning a Hidden Hypergraph
• Learning a Mahalanobis Metric from Equivalence Constraints
• Learning from Examples as an Inverse Problem
• Learning the Kernel Function via Regularization
• Learning the Kernel with Hyperkernels
• Learning to Construct Fast Signal Processing Implementations
• Learning to Detect and Classify Malicious Executables in the Wild
• Learning with Decision Lists of Data-Dependent Features
• Learning with Mixtures of Trees
• Limitations of Learning Via Emb eddings in Euclidean Half Spaces
• Linear Programming Relaxations and Belief Propagation ­ An Empirical Study
• Linear Programs for Hypotheses Selection in Probabilistic Inference Models
• Linear State-Space Models for Blind Source Separation
• Local Propagation in Conditional Gaussian Bayesian Networks
• Loopy Belief Propagation: Convergence and Effects of Message Errors
• Lower Bounds and Aggregation in Density Estimation
• Lyapunov Design for Safe Reinforcement Learning
• MLPs (Mono-Layer Polynomials and Multi-Layer Perceptrons) for Nonlinear Modeling
• Machine Learning Methods for Predicting Failures in Hard Drives: A Multiple-Instance Application
• Machine Learning for Computer Security
• Machine Learning with Data Dep endent Hyp othesis Classes
• Machine Learning with Data Dep endent Hyp othesis Classes
• Managing Diversity in Regression Ensembles
• Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
• Margin Trees for High-dimensional Classification
• Matching Words and Pictures
• Matrix Exponentiated Gradient Updates for On-line Learning and Bregman Projection
• Maximum Margin Algorithms with Boolean Kernels
• Maximum-Gain Working Set Selection for SVMs
• Memory-Based Shallow Parsing
• MinReg: A Scalable Algorithm for Learning Parsimonious Regulatory Networks in Yeast and Mammals
• Minimal Kernel Classifiers
• Minimal Kernel Classifiers
• Minimax Regret Classifier for Imprecise Class Distributions
• Model Averaging for Prediction with Discrete Bayesian Networks
• Multi-Task Learning for Classification with Dirichlet Process Priors
• Multiclass Boosting for Weak Classifiers
• Multiclass Classification with Multi-Prototype Support Vector Machines
• Multiple-Instance Learning of Real-Valued Data
• New Algorithms for Efficient High-Dimensional Nonparametric Classification
• New Horn Revision Algorithms
• No Unbiased Estimator of the Variance of K-Fold Cross-Validation
• Noise Tolerant Variants of the Perceptron Algorithm
• Noisy-OR Component Analysis and its Application to Link Analysis
• Non-negative Matrix Factorization with Sparseness Constraints
• Nonlinear Boosting Projections for Ensemble Construction
• Nonparametric Quantile Estimation
• On Bo osting with Polynomially Bounded Distributions
• On Inferring Application Protocol Behaviors in Encrypted Network Traffic
• On Model Selection Consistency of Lasso
• On Online Learning of Decision Lists
• On Online Learning of Decision Lists
• On Representing and Generating Kernels by Fuzzy Equivalence Relations
• On Robustness Properties of Convex Risk Minimization Methods for Pattern Recognition
• On Using Extended Statistical Queries to Avoid Memb ership Queries
• On Using Extended Statistical Queries to Avoid Memb ership Queries
• On the Algorithmic Implementation of Multiclass Kernel-based Vector Machines
• On the Complexity of Learning Lexicographic Strategies
• On the Convergence of Optimistic Policy Iteration
• On the Convergence of Optimistic Policy Iteration
• On the Influence of the Kernel on the Consistency of Supp ort Vector Machines
• On the Influence of the Kernel on the Consistency of Supp ort Vector Machines
• One-Class Novelty Detection for Seizure Analysis from Intracranial EEG
• One-Class SVMs for Do cument Classification
• Online Passive-Aggressive Algorithms
• Optimal Structure Identification With Greedy Search
• Optimal Structure Identification With Greedy Search
• Optimising Kernel Parameters and Regularisation Coefficients for Non-linear Discriminant Analysis
• Overfitting in Making Comparisons Between Variable Selection Methods
• PAC-Bayesian Generalisation Error Bounds for Gaussian Pro cess Classification
• PAC-Bayesian Generalisation Error Bounds for Gaussian Pro cess Classification
• Parallel Software for Training Large Scale Support Vector Machines on Multiprocessor Systems
• Point-Based Value Iteration for Continuous POMDPs
• Policy Gradient in Continuous Time
• Policy Search using Paired Comparisons
• Preventing Over-Fitting during Model Selection via Bayesian Regularisation of the Hyper-Parameters
• Prior Knowledge and Preferential Structures in Gradient Descent Learning Algorithms
• Prior Knowledge and Preferential Structures in Gradient Descent Learning Algorithms
• Prioritization Methods for Accelerating MDP Solvers
• Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models
• Probability Estimates for Multi-class Classification by Pairwise Coupling
• Probability Product Kernels
• QP Algorithms with Guaranteed Accuracy and Run Time for Support Vector Machines
• Quantile Regression Forests
• Quasi-Geodesic Neural Learning Algorithms Over the Orthogonal Group: A Tutorial
• R-max ­ A General Polynomial Time Algorithm for Near-Optimal Reinforcement Learning
• R-max ­ A General Polynomial Time Algorithm for Near-Optimal Reinforcement Learning
• Rademacher and Gaussian Complexities: Risk Bounds and Structural Results
• Randomized Variable Elimination
• Ranking a Random Feature for Variable and Feature Selection
• Rational Kernels: Theory and Algorithms
• Rearrangement Clustering: Pitfalls, Remedies, and Applications
• Recommender Systems Using Linear Classifiers
• Recommender Systems Using Linear Classifiers
• Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers
• Regularized Principal Manifolds
• Reinforcement Learning with Factored States and Actions
• Relational Dependency Networks
• Round Robin Classification
• Round Robin Classification
• SVMTorch: Support Vector Machines for Large-Scale Regression Problems
• Second Order Cone Programming Approaches for Handling Missing and Uncertain Data
• Second Order Cone Programming Formulations for Feature Selection
• Segmental Hidden Markov Models with Random Effects for Waveform Modeling
• Selective Rademacher Penalization and Reduced Error Pruning of Decision Trees
• Semigroup Kernels on Measures
• Separating Models of Learning from Correlated and Uncorrelated Data
• Separating a Real-Life Nonlinear Image Mixture
• Shallow Parsing using Noisy and Non-Stationary Training Material
• Shallow Parsing using Sp ecialized HMMs
• Shallow Parsing with PoS Taggers and Linguistic Features
• Smo oth -Insensitive Regression by Loss Symmetrization
• Some Dichotomy Theorems for Neural Learning Problems
• Some Discriminant-Based PAC Algorithms
• Some Greedy Learning Algorithms for Sparse Regression and Classification with Mercer Kernels
• Some Properties of Regularized Kernel Methods
• Some Theory for Generalized Boosting Algorithms
• Spam Filtering Based On The Analysis Of Text Information Embedded Into Images
• Spam Filtering Using Statistical Data Compression Models
• Sparse Boosting
• Sparseness vs Estimating Conditional Probabilities: Some Asymptotic Results
• Stability Properties of Empirical Risk Minimization over Donsker Classes
• Stability and Generalization
• Stability and Generalization
• Stability of Randomized Learning Algorithms
• Statistical Analysis of Some Multi-Category Large Margin Classification Methods
• Statistical Comparisons of Classifiers over Multiple Data Sets
• Statistical Consistency of Kernel Canonical Correlation Analysis
• Step Size Adaptation in Reproducing Kernel Hilbert Space
• Stochastic Complexities of Gaussian Mixtures in Variational Bayesian Approximation
• Stopping Criterion for Boosting-Based Data Reduction Techniques: from Binary to Multiclass Problems
• Streamwise Feature Selection
• Structured Prediction, Dual Extragradient and Bregman Projections
• Sufficient Dimensionality Reduction
• Superior Guarantees for Sequential Prediction and Lossless Compression via Alphabet Decomposition
• Supp ort Vector Clustering
• Supp ort Vector Clustering
• Supp ort Vector Machine Active Learning with Applications to Text Classification
• Supp ort Vector Machine Active Learning with Applications to Text Classification
• Support Vector Machine Soft Margin Classifiers: Error Analysis
• Text Chunking based on a Generalization of Winnow
• Text Classification using String Kernels
• Text Classification using String Kernels
• The Dynamics of AdaBoost: Cyclic Behavior and Convergence of Margins
• The Entire Regularization Path for the Support Vector Machine
• The Interplay of Optimization and Machine Learning Research
• The Learning-Curve Sampling Metho d Applied to Mo del-Based Clustering
• The Learning-Curve Sampling Metho d Applied to Mo del-Based Clustering
• The Minimum Error Minimax Probability Machine
• The Pyramid Match Kernel: Efficient Learning with Sets of Features
• The Representational Power of Discrete Bayesian Networks
• The Set Covering Machine
• The Subspace Information Criterion for Infinite Dimensional Hyp othesis Spaces
• The Subspace Information Criterion for Infinite Dimensional Hyp othesis Spaces
• Toward Attribute Efficient Learning of Decision Lists and Parities
• Tracking a Small Set of Exp erts by Mixing Past Posteriors
• Tree-Based Batch Mode Reinforcement Learning
• Tutorial on Practical Prediction Theory for Classification
• Ultraconservative Online Algorithms for Multiclass Problems
• Ultraconservative Online Algorithms for Multiclass Problems
• Uniform Ob ject Generation for Optimizing One-class Classifiers
• Universal Algorithms for Learning Theory Part I : Piecewise Constant Functions
• Universal Kernels
• Use of the Zero-Norm with Linear Models and Kernel Methods
• Using Confidence Bounds for Exploitation-Exploration Trade-offs
• Using Machine Learning to Guide Architecture Simulation
• Value Regularization and Fenchel Duality
• Variable Selection Using SVM-based Criteria
• Variance Reduction Techniques for Gradient Estimates in Reinforcement Learning
• Variational Learning of Clusters of Undercomplete Nonsymmetric Independent Components
• Variational Learning of Clusters of Undercomplete Nonsymmetric Independent Components
• Variational Message Passing
• Walk-Sums and Belief Propagation in Gaussian Graphical Models
• What's Strange About Recent Events (WSARE): An Algorithm for the Early Detection of Disease Outbreaks
• Word-Sequence Kernels
• Working Set Selection Using Second Order Information for Training Supp ort Vector Machines
• Worst-Case Analysis of Selective Sampling for Linear Classification
• ¨ On the Nystrom Method for Approximating a Gram Matrix for Improved Kernel-Based Learning
• ­MDPs: Learning in Varying Environments
• ­MDPs: Learning in Varying Environments