model selection

Predictive Matrix-Variate t Models NeurIPS 2007 Preventing Over-Fitting during Model Selection via Bayesian Regularisation of the Hyper-Parameters JMLR 2007 Progressive mixture rules are deviation suboptimal NeurIPS 2007 Analysis of Empirical Bayesian Methods for Neuroelectromagnetic Source Localization NeurIPS 2006 An Efficient Method for Gradient-Based Adaptation of Hyperparameters in SVM Models NeurIPS 2006 Bayesian Model Scoring in Markov Random Fields NeurIPS 2006 Denoising and Dimension Reduction in Feature Space NeurIPS 2006 Large Scale Multiple Kernel Learning JMLR 2006 Lower Bounds and Aggregation in Density Estimation JMLR 2006 On Model Selection Consistency of Lasso JMLR 2006 PG-means: learning the number of clusters in data NeurIPS 2006 Sparse Boosting JMLR 2006 Stochastic Complexities of Gaussian Mixtures in Variational Bayesian Approximation JMLR 2006 Asymptotic Model Selection for Naive Bayesian Networks JMLR 2005 Learning with Decision Lists of Data-Dependent Features JMLR 2005 A Compression Approach to Support Vector Model Selection JMLR 2004 Computable Shell Decomposition Bounds JMLR 2004 Learning the Kernel Matrix with Semidefinite Programming JMLR 2004 Online Choice of Active Learning Algorithms JMLR 2004 The Entire Regularization Path for the Support Vector Machine JMLR 2004 Extensions to Metric-Based Model Selection JMLR 2003 MLPs (Mono-Layer Polynomials and Multi-Layer Perceptrons) for Nonlinear Modeling JMLR 2003 Overfitting in Making Comparisons Between Variable Selection Methods JMLR 2003 Data-dependent margin-based generalization bounds for classification JMLR 2002 Learning Equivalence Classes of Bayesian-Network Structures JMLR 2002