Kernel Methods

Kernel Hyperalignment NeurIPS 2012 Kernel Latent SVM for Visual Recognition NeurIPS 2012 Learning from Distributions via Support Measure Machines NeurIPS 2012 Learning Networks of Heterogeneous Influence NeurIPS 2012 Multiple Operator-valued Kernel Learning NeurIPS 2012 Non-parametric Approximate Dynamic Programming via the Kernel Method NeurIPS 2012 Nyström Method vs Random Fourier Features: A Theoretical and Empirical Comparison NeurIPS 2012 On-line Reinforcement Learning Using Incremental Kernel-Based Stochastic Factorization NeurIPS 2012 Optimal kernel choice for large-scale two-sample tests NeurIPS 2012 PAC-Bayesian Bound for Gaussian Process Regression and Multiple Kernel Additive Model COLT 2012 Perceptron Learning of SAT NeurIPS 2012 Persistent Homology for Learning Densities with Bounded Support NeurIPS 2012 Q-MKL: Matrix-induced Regularization in Multi-Kernel Learning with Applications to Neuroimaging NeurIPS 2012 Random Feature Maps for Dot Product Kernels AISTATS 2012 Scaling up Kernel SVM on Limited Resources: A Low-rank Linearization Approach AISTATS 2012 Semantic Kernel Forests from Multiple Taxonomies NeurIPS 2012 Supervised Learning with Similarity Functions NeurIPS 2012 The Lovász ϑ function, SVMs and finding large dense subgraphs NeurIPS 2012 The representer theorem for Hilbert spaces: a necessary and sufficient condition NeurIPS 2012 A Non-Parametric Approach to Dynamic Programming NeurIPS 2011 Efficient and Effective Visual Codebook Generation Using Additive Kernels JMLR 2011 Generalizing from Several Related Classification Tasks to a New Unlabeled Sample NeurIPS 2011 Generative Kernels for Exponential Families AISTATS 2011 Improved Loss Bounds For Multiple Kernel Learning AISTATS 2011 Kernel Bayes' Rule NeurIPS 2011