This introduction to the theory of variational Bayesian learning summarizes recent developments and suggests practical applications.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Shinichi Nakajima is a senior researcher at Technische Universität Berlin. His research interests include the theory and applications of machine learning, and he has published papers at numerous conferences and in journals such as the Journal of Machine Learning Research, the Machine Learning Journal, Neural Computation, and IEEE Transactions on Signal Processing. He currently serves as an area chair for NIPS and an action Editor for Digital Signal Processing.
Inhaltsangabe
1. Bayesian learning 2. Variational Bayesian learning 3. VB algorithm for multi-linear models 4. VB Algorithm for latent variable models 5. VB algorithm under No Conjugacy 6. Global VB solution of fully observed matrix factorization 7. Model-induced regularization and sparsity inducing mechanism 8. Performance analysis of VB matrix factorization 9. Global solver for matrix factorization 10. Global solver for low-rank subspace clustering 11. Efficient solver for sparse additive matrix factorization 12. MAP and partially Bayesian learning 13. Asymptotic Bayesian learning theory 14. Asymptotic VB theory of reduced rank regression 15. Asymptotic VB theory of mixture models 16. Asymptotic VB theory of other latent variable models 17. Unified theory.
1. Bayesian learning 2. Variational Bayesian learning 3. VB algorithm for multi-linear models 4. VB Algorithm for latent variable models 5. VB algorithm under No Conjugacy 6. Global VB solution of fully observed matrix factorization 7. Model-induced regularization and sparsity inducing mechanism 8. Performance analysis of VB matrix factorization 9. Global solver for matrix factorization 10. Global solver for low-rank subspace clustering 11. Efficient solver for sparse additive matrix factorization 12. MAP and partially Bayesian learning 13. Asymptotic Bayesian learning theory 14. Asymptotic VB theory of reduced rank regression 15. Asymptotic VB theory of mixture models 16. Asymptotic VB theory of other latent variable models 17. Unified theory.
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