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Cover: 9781107076150 | Variational Bayesian Learning Theory | Shinichi Nakajima (u. a.)
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Variational Bayesian Learning Theory
Buch von Shinichi Nakajima (u. a.)
Sprache: Englisch

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Beschreibung
This introduction to the theory of variational Bayesian learning summarizes recent developments and suggests practical applications.
This introduction to the theory of variational Bayesian learning summarizes recent developments and suggests practical applications.
Über den Autor
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.
Inhaltsverzeichnis
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.
Details
Erscheinungsjahr: 2019
Genre: Informatik
Rubrik: Naturwissenschaften & Technik
Medium: Buch
ISBN-13: 9781107076150
ISBN-10: 1107076153
Sprache: Englisch
Ausstattung / Beilage: HC gerader Rücken kaschiert
Einband: Gebunden
Autor: Nakajima, Shinichi
Watanabe, Kazuho
Sugiyama, Masashi
Hersteller: Cambridge University Press
Maße: 235 x 157 x 37 mm
Von/Mit: Shinichi Nakajima (u. a.)
Erscheinungsdatum: 11.06.2019
Gewicht: 1,052 kg
Artikel-ID: 115388150
Über den Autor
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.
Inhaltsverzeichnis
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.
Details
Erscheinungsjahr: 2019
Genre: Informatik
Rubrik: Naturwissenschaften & Technik
Medium: Buch
ISBN-13: 9781107076150
ISBN-10: 1107076153
Sprache: Englisch
Ausstattung / Beilage: HC gerader Rücken kaschiert
Einband: Gebunden
Autor: Nakajima, Shinichi
Watanabe, Kazuho
Sugiyama, Masashi
Hersteller: Cambridge University Press
Maße: 235 x 157 x 37 mm
Von/Mit: Shinichi Nakajima (u. a.)
Erscheinungsdatum: 11.06.2019
Gewicht: 1,052 kg
Artikel-ID: 115388150
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