Statistical Learning

A new inlier identification scheme for robust estimation problems RSS 2006 An Information Theoretic Framework for Eukaryotic Gradient Sensing NeurIPS 2006 An Oracle Inequality for Clipped Regularized Risk Minimizers NeurIPS 2006 A PAC-Bayes Risk Bound for General Loss Functions NeurIPS 2006 Bounds for Linear Multi-Task Learning JMLR 2006 Bounds for the Loss in Probability of Correct Classification Under Model Based Approximation JMLR 2006 Consistency and Convergence Rates of One-Class SVMs and Related Algorithms JMLR 2006 Consistency of Multiclass Empirical Risk Minimization Methods Based on Convex Loss JMLR 2006 Geometric entropy minimization (GEM) for anomaly detection and localization NeurIPS 2006 High-Dimensional Graphical Model Selection Using ℓ_1-Regularized Logistic Regression NeurIPS 2006 Kernels on Prolog Proof Trees: Statistical Learning in the ILP Setting JMLR 2006 Learning Coordinate Covariances via Gradients JMLR 2006 Learning from Multiple Sources NeurIPS 2006 Learning Minimum Volume Sets JMLR 2006 Lower Bounds and Aggregation in Density Estimation JMLR 2006 MinReg: A Scalable Algorithm for Learning Parsimonious Regulatory Networks in Yeast and Mammals JMLR 2006 Non-rigid point set registration: Coherent Point Drift NeurIPS 2006 On the Relation Between Low Density Separation, Spectral Clustering and Graph Cuts NeurIPS 2006 PAC-Bayes Bounds for the Risk of the Majority Vote and the Variance of the Gibbs Classifier NeurIPS 2006 Pattern Recognition for Conditionally Independent Data JMLR 2006 PG-means: learning the number of clusters in data NeurIPS 2006 Quantile Regression Forests JMLR 2006 Segmental Hidden Markov Models with Random Effects for Waveform Modeling JMLR 2006 Shifting, One-Inclusion Mistake Bounds and Tight Multiclass Expected Risk Bounds NeurIPS 2006 Some Discriminant-Based PAC Algorithms JMLR 2006