Supervised Learning

Linear Classification and Selective Sampling Under Low Noise Conditions NeurIPS 2008 Localized Sliced Inverse Regression NeurIPS 2008 On the Efficient Minimization of Classification Calibrated Surrogates NeurIPS 2008 Partially Observed Maximum Entropy Discrimination Markov Networks NeurIPS 2008 Privacy-preserving logistic regression NeurIPS 2008 Support Vector Machines with a Reject Option NeurIPS 2008 A Constraint Generation Approach to Learning Stable Linear Dynamical Systems NeurIPS 2007 A general agnostic active learning algorithm NeurIPS 2007 A learning framework for nearest neighbor search NeurIPS 2007 An Analysis of Inference with the Universum NeurIPS 2007 Anytime Induction of Cost-sensitive Trees NeurIPS 2007 Boosting Algorithms for Maximizing the Soft Margin NeurIPS 2007 Compressed Regression NeurIPS 2007 Dimensionality Reduction of Multimodal Labeled Data by Local Fisher Discriminant Analysis JMLR 2007 Discriminative Log-Linear Grammars with Latent Variables NeurIPS 2007 Efficient multiple hyperparameter learning for log-linear models NeurIPS 2007 FilterBoost: Regression and Classification on Large Datasets NeurIPS 2007 Hierarchical Penalization NeurIPS 2007 Integrating Naïve Bayes and FOIL JMLR 2007 Learning Horn Expressions with LOGAN-H JMLR 2007 Learning Monotonic Transformations for Classification NeurIPS 2007 Learning to classify complex patterns using a VLSI network of spiking neurons NeurIPS 2007 One-Pass Boosting NeurIPS 2007 Separating Models of Learning from Correlated and Uncorrelated Data JMLR 2007 SpAM: Sparse Additive Models NeurIPS 2007