Support Vector Machine

Tighter Bounds for Structured Estimation NeurIPS 2008 An Analysis of Inference with the Universum NeurIPS 2007 A Randomized Algorithm for Large Scale Support Vector Learning NeurIPS 2007 A Risk Minimization Principle for a Class of Parzen Estimators NeurIPS 2007 Bundle Methods for Machine Learning NeurIPS 2007 Comments on the “Core Vector Machines: Fast SVM Training on Very Large Data Sets" JMLR 2007 Configuration Estimates Improve Pedestrian Finding NeurIPS 2007 Convex Learning with Invariances NeurIPS 2007 Discriminative Keyword Selection Using Support Vector Machines NeurIPS 2007 Efficient Convex Relaxation for Transductive Support Vector Machine NeurIPS 2007 How SVMs can estimate quantiles and the median NeurIPS 2007 Learning with Transformation Invariant Kernels NeurIPS 2007 Margin Trees for High-dimensional Classification JMLR 2007 Multi-Task Learning via Conic Programming NeurIPS 2007 On the Representer Theorem and Equivalent Degrees of Freedom of SVR JMLR 2007 Parallelizing Support Vector Machines on Distributed Computers NeurIPS 2007 Support Vector Machine Classification with Indefinite Kernels NeurIPS 2007 A Direct Method for Building Sparse Kernel Learning Algorithms JMLR 2006 A Hierarchy of Support Vector Machines for Pattern Detection JMLR 2006 An Efficient Method for Gradient-Based Adaptation of Hyperparameters in SVM Models NeurIPS 2006 An Oracle Inequality for Clipped Regularized Risk Minimizers NeurIPS 2006 Branch and Bound for Semi-Supervised Support Vector Machines NeurIPS 2006 Building Support Vector Machines with Reduced Classifier Complexity JMLR 2006 Considering Cost Asymmetry in Learning Classifiers JMLR 2006 Consistency and Convergence Rates of One-Class SVMs and Related Algorithms JMLR 2006