1532-4435

Journal of Machine Learning Research （JMLR） - Issue 11 论文列表

点击这里查看 Journal of Machine Learning Research 的JCR分区、影响因子等信息
卷期号： Issue 11
发布时间：
卷期年份： 2006
卷期官网：
本期论文列表
Exact 1-Norm Support Vector Machines Via Unconstrained Convex Differentiable Minimization.

Lower Bounds and Aggregation in Density Estimation.

The Interplay of Optimization and Machine Learning Research.

Considering Cost Asymmetry in Learning Classifiers.

Fast SDP Relaxations of Graph Cut Clustering, Transduction, and Other Combinatorial Problem.

Point-Based Value Iteration for Continuous POMDPs.

Incremental Algorithms for Hierarchical Classification.

Machine Learning for Computer Security.

Estimating the "Wrong" Graphical Model: Benefits in the Computation-Limited Setting.

Kernels on Prolog Proof Trees: Statistical Learning in the ILP Setting.

Some Theory for Generalized Boosting Algorithms.

Learning Sparse Representations by Non-Negative Matrix Factorization and Sequential Cone Programming.

Quantile Regression Forests.

Pattern Recognition for Conditionally Independent Data.

Computational and Theoretical Analysis of Null Space and Orthogonal Linear Discriminant Analysis.

Toward Attribute Efficient Learning of Decision Lists and Parities.

A Robust Procedure For Gaussian Graphical Model Search From Microarray Data With

A Simulation-Based Algorithm for Ergodic Control of Markov Chains Conditioned on Rare Events.

Learning Recursive Control Programs from Problem Solving.

Step Size Adaptation in Reproducing Kernel Hilbert Space.

Sparse Boosting.

Maximum-Gain Working Set Selection for SVMs.

Parallel Software for Training Large Scale Support Vector Machines on Multiprocessor Systems.

Learning Minimum Volume Sets.

Large Scale Transductive SVMs.

Spam Filtering Based On The Analysis Of Text Information Embedded Into Images.

Accurate Error Bounds for the Eigenvalues of the Kernel Matrix.

Some Discriminant-Based PAC Algorithms.

Nonparametric Quantile Estimation.

Learning to Detect and Classify Malicious Executables in the Wild.

Adaptive Prototype Learning Algorithms: Theoretical and Experimental Studies.

Second Order Cone Programming Approaches for Handling Missing and Uncertain Data.

Learning Parts-Based Representations of Data.

An Efficient Implementation of an Active Set Method for SVMs.

On Representing and Generating Kernels by Fuzzy Equivalence Relations.

Bounds for Linear Multi-Task Learning.

Learning Spectral Clustering, With Application To Speech Separation.

Optimising Kernel Parameters and Regularisation Coefficients for Non-linear Discriminant Analysis.

Statistical Comparisons of Classifiers over Multiple Data Sets.

Consistency of Multiclass Empirical Risk Minimization Methods Based on Convex Loss.

A Hierarchy of Support Vector Machines for Pattern Detection.

Building Support Vector Machines with Reduced Classifier Complexity.

Superior Guarantees for Sequential Prediction and Lossless Compression via Alphabet Decomposition.

Learning a Hidden Hypergraph.

Linear State-Space Models for Blind Source Separation.

Universal Kernels.

Generalized Bradley-Terry Models and Multi-Class Probability Estimates.

Expectation Correction for Smoothed Inference in Switching Linear Dynamical Systems.

A Very Fast Learning Method for Neural Networks Based on Sensitivity Analysis.

Infinite-sigma Limits For Tikhonov Regularization.

Walk-Sums and Belief Propagation in Gaussian Graphical Models.

Stability Properties of Empirical Risk Minimization over Donsker Classes.

Active Learning in Approximately Linear Regression Based on Conditional Expectation of Generalization Error.

Causal Graph Based Decomposition of Factored MDPs.

Estimation of Gradients and Coordinate Covariation in Classification.

Policy Gradient in Continuous Time.

Using Machine Learning to Guide Architecture Simulation.

A Scoring Function for Learning Bayesian Networks based on Mutual Information and Conditional Independence Tests.

Inductive Synthesis of Functional Programs: An Explanation Based Generalization Approach.

Geometric Variance Reduction in Markov Chains: Application to Value Function and Gradient Estimation.

One-Class Novelty Detection for Seizure Analysis from Intracranial EEG.

Stochastic Complexities of Gaussian Mixtures in Variational Bayesian Approximation.

In Search of Non-Gaussian Components of a High-Dimensional Distribution.

Linear Programs for Hypotheses Selection in Probabilistic Inference Models.

Consistency and Convergence Rates of One-Class SVMs and Related Algorithms.

A Graphical Representation of Equivalence Classes of AMP Chain Graphs.

On the Complexity of Learning Lexicographic Strategies.

Noisy-OR Component Analysis and its Application to Link Analysis.

Learning Image Components for Object Recognition.

QP Algorithms with Guaranteed Accuracy and Run Time for Support Vector Machines.

Incremental Support Vector Learning: Analysis, Implementation and Applications.

Bounds for the Loss in Probability of Correct Classification Under Model Based Approximation.

Ensemble Pruning Via Semi-definite Programming.

Active Learning with Feedback on Features and Instances.

Learning the Structure of Linear Latent Variable Models.

Distance Patterns in Structural Similarity.

A Linear Non-Gaussian Acyclic Model for Causal Discovery.

Rearrangement Clustering: Pitfalls, Remedies, and Applications.

Evolutionary Function Approximation for Reinforcement Learning.

Large Scale Multiple Kernel Learning.

Spam Filtering Using Statistical Data Compression Models.

Collaborative Multiagent Reinforcement Learning by Payoff Propagation.

Online Passive-Aggressive Algorithms.

MinReg: A Scalable Algorithm for Learning Parsimonious Regulatory Networks in Yeast and Mammals.

New Algorithms for Efficient High-Dimensional Nonparametric Classification.

Bayesian Network Learning with Parameter Constraints.

A Direct Method for Building Sparse Kernel Learning Algorithms.

Learning Coordinate Covariances via Gradients.

Efficient Learning of Label Ranking by Soft Projections onto Polyhedra.

Worst-Case Analysis of Selective Sampling for Linear Classification.

Streamwise Feature Selection.

Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples.

Segmental Hidden Markov Models with Random Effects for Waveform Modeling.

Learning Factor Graphs in Polynomial Time and Sample Complexity.

Structured Prediction, Dual Extragradient and Bregman Projections.

Linear Programming Relaxations and Belief Propagation - An Empirical Study.

Action Elimination and Stopping Conditions for the Multi-Armed Bandit and Reinforcement Learning Problems.

On Model Selection Consistency of Lasso.

Kernel-Based Learning of Hierarchical Multilabel Classification Models.

On Inferring Application Protocol Behaviors in Encrypted Network Traffic.