著者: Matthias W. Seeger - Conference on Neural Information Processing Systems, NeurIPS 2001 被引用: 87
We propose the framework of mutual information kernels for learning covariance kernels, as used in Support Vector machines and Gaussian process classifiers, from unlabeled task data using Bayesian techniques. We describe an implementation of this framework which uses variational Bayesian mixtures of factor analyzers in order to attack classification problems in high-dimensional spaces where labeled data is sparse, but unlabeled data is abundant. 1
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Numerical recipes in C Numerical recipes in C Support-Vector Networks Support-Vector Networks Exploiting Generative Models in Discriminativ… Exploiting Generative Models in Discriminative Classifiers Bayesian Model Selection for Support Vector… Bayesian Model Selection for Support Vector Machines, Gaussian Processes and Other Kernel Classifiers Statistical Learning Theory Statistical Learning Theory Variational Inference for Bayesian Mixtures o… Variational Inference for Bayesian Mixtures of Factor Analysers Using the Fisher Kernel Method to Detect Remote… Using the Fisher Kernel Method to Detect Remote Protein Homologies Convolution kernels on discrete structures Convolution kernels on discrete structures Dynamic Alignment Kernels Dynamic Alignment Kernels Regularization with Dot-Product Kernels Regularization with Dot-Product Kernels A New Discriminative Kernel From… A New Discriminative Kernel From Probabilistic Models Learning from Labeled and Unlabeled Data Learning from Labeled and Unlabeled Data Cluster Kernels for Semi-Supervised Learning Cluster Kernels for Semi-Supervised Learning Bayesian Gaussian process models… Bayesian Gaussian process models : PAC-Bayesian generalisation error bounds and sparse approximations Gaussian Processes For Machine Learning Gaussian Processes For Machine Learning Asymptotic Properties of the Fisher Kernel Asymptotic Properties of the Fisher Kernel A mutual information kernel for sequences A mutual information kernel for sequences Extensions of the Informative Vector… Extensions of the Informative Vector Machine The context-tree kernel for strings The context-tree kernel for strings Semi-Supervised Learning Semi-Supervised Learning Using Deep Belief Nets to Learn Covariance… Using Deep Belief Nets to Learn Covariance Kernels for Gaussian Processes Learning from Labeled and Unlabeled Data Learning from Labeled and Unlabeled Data Learning Deep Generative Models Learning Deep Generative Models Learning from Labeled and Unlabeled Data Learning from Labeled and Unlabeled Data Covariance Kernels from Bayesian Generative… Covariance Kernels from Bayesian Generative Models 過去の参考文献 中心の論文 この論文を引用する論文 古い 新しい ノードをクリックするとフォーカスを固定、空白をクリックすると本論文に戻ります。ホバーで一時的にプレビューできます。各ノードのページはタイトルから開けます。
