kernel methods

Gaussian and Wishart Hyperkernels NeurIPS 2006 Generalized Maximum Margin Clustering and Unsupervised Kernel Learning NeurIPS 2006 Generalized Regularized Least-Squares Learning with Predefined Features in a Hilbert Space NeurIPS 2006 Hyperparameter Learning for Graph Based Semi-supervised Learning Algorithms NeurIPS 2006 implicit Online Learning with Kernels NeurIPS 2006 Infinite-σ Limits For Tikhonov Regularization JMLR 2006 Kernel-Based Learning of Hierarchical Multilabel Classification Models JMLR 2006 Kernel Maximum Entropy Data Transformation and an Enhanced Spectral Clustering Algorithm NeurIPS 2006 Kernels on Prolog Proof Trees: Statistical Learning in the ILP Setting JMLR 2006 Kernels on Structured Objects Through Nested Histograms NeurIPS 2006 Large Margin Component Analysis NeurIPS 2006 Large Scale Multiple Kernel Learning JMLR 2006 Large-Scale Sparsified Manifold Regularization NeurIPS 2006 Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples JMLR 2006 Mutagenetic tree Fisher kernel improves prediction of HIV drug resistance from viral genotype NeurIPS 2006 On Representing and Generating Kernels by Fuzzy Equivalence Relations JMLR 2006 On Transductive Regression NeurIPS 2006 Optimising Kernel Parameters and Regularisation Coefficients for Non-linear Discriminant Analysis JMLR 2006 Randomized PCA Algorithms with Regret Bounds that are Logarithmic in the Dimension NeurIPS 2006 Relational Learning with Gaussian Processes NeurIPS 2006 Sparse Kernel Orthonormalized PLS for feature extraction in large data sets NeurIPS 2006 Universal Kernels JMLR 2006 Core Vector Machines: Fast SVM Training on Very Large Data Sets JMLR 2005 Diffusion Kernels on Statistical Manifolds JMLR 2005 Frames, Reproducing Kernels, Regularization and Learning JMLR 2005