Statistical Learning

Some Theory for Generalized Boosting Algorithms JMLR 2006 Stability Properties of Empirical Risk Minimization over Donsker Classes JMLR 2006 Streamwise Feature Selection JMLR 2006 Tighter PAC-Bayes Bounds NeurIPS 2006 Analysis of Variance of Cross-Validation Estimators of the Generalization Error JMLR 2005 Asymptotic Model Selection for Naive Bayesian Networks JMLR 2005 Asymptotics in Empirical Risk Minimization JMLR 2005 Concentration Bounds for Unigram Language Models JMLR 2005 Diffusion Kernels on Statistical Manifolds JMLR 2005 Estimating Functions for Blind Separation When Sources Have Variance Dependencies JMLR 2005 Estimation of Non-Normalized Statistical Models by Score Matching JMLR 2005 Learning from Examples as an Inverse Problem JMLR 2005 Learning Multiple Tasks with Kernel Methods JMLR 2005 A Compression Approach to Support Vector Model Selection JMLR 2004 Bias-Variance Analysis of Support Vector Machines for the Development of SVM-Based Ensemble Methods JMLR 2004 Computable Shell Decomposition Bounds JMLR 2004 No Unbiased Estimator of the Variance of K-Fold Cross-Validation JMLR 2004 Statistical Analysis of Some Multi-Category Large Margin Classification Methods JMLR 2004 Support Vector Machine Soft Margin Classifiers: Error Analysis JMLR 2004 The Sample Complexity of Exploration in the Multi-Armed Bandit Problem JMLR 2004 A Maximum Likelihood Approach to Single-channel Source Separation JMLR 2003 Comparing Bayes Model Averaging and Stacking When Model Approximation Error Cannot be Ignored JMLR 2003 Concentration Inequalities for the Missing Mass and for Histogram Rule Error JMLR 2003 Feature Extraction by Non-Parametric Mutual Information Maximization JMLR 2003 Generalization Error Bounds for Bayesian Mixture Algorithms JMLR 2003