Variational Bayesian Learning Theory

· ·
· Cambridge University Press
E-grāmata
561
Lappuses

Par šo e-grāmatu

Variational Bayesian learning is one of the most popular methods in machine learning. Designed for researchers and graduate students in machine learning, this book summarizes recent developments in the non-asymptotic and asymptotic theory of variational Bayesian learning and suggests how this theory can be applied in practice. The authors begin by developing a basic framework with a focus on conjugacy, which enables the reader to derive tractable algorithms. Next, it summarizes non-asymptotic theory, which, although limited in application to bilinear models, precisely describes the behavior of the variational Bayesian solution and reveals its sparsity inducing mechanism. Finally, the text summarizes asymptotic theory, which reveals phase transition phenomena depending on the prior setting, thus providing suggestions on how to set hyperparameters for particular purposes. Detailed derivations allow readers to follow along without prior knowledge of the mathematical techniques specific to Bayesian learning.
Variational Bayesian learning is one of the most popular methods in machine learning. Designed for researchers and graduate students in machine learning, this book summarizes recent developments in the non-asymptotic and asymptotic theory of variational Bayesian learning and suggests how this the...

Citas grāmatas

Par autoru

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.

Kazuho Watanabe is a lecturer at Toyohashi University of Technology. His research interests include statistical machine learning and information theory, and he has published papers at numerous conferences and in journals such as the Journal of Machine Learning Research, the Machine Learning Journal, IEEE Transactions on Information Theory, and IEEE Transactions on Neural Networks and Learning Systems.

Masashi Sugiyama is Director of the RIKEN Center for Advanced Intelligence Project and Professor of Complexity Science and Engineering at the University of Tokyo. His research interests include the theory, algorithms, and applications of machine learning. He has written several books on machine learning, including Density Ratio Estimation in Machine Learning (Cambridge, 2012). He served as program co-chair and general co-chair of the NIPS conference in 2015 and 2016, respectively, and received the Japan Academy Medal in 2017.

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 Jou...

Novērtējiet šo e-grāmatu

Izsakiet savu viedokli!

Informācija lasīšanai

Viedtālruņi un planšetdatori
Instalējiet lietotni Google Play grāmatas Android ierīcēm un iPad planšetdatoriem/iPhone tālruņiem. Lietotne tiks automātiski sinhronizēta ar jūsu kontu un ļaus lasīt saturu tiešsaistē vai bezsaistē neatkarīgi no jūsu atrašanās vietas.
Klēpjdatori un galddatori
Varat klausīties pakalpojumā Google Play iegādātās audiogrāmatas, izmantojot datora tīmekļa pārlūkprogrammu.
E-lasītāji un citas ierīces
Lai lasītu grāmatas tādās elektroniskās tintes ierīcēs kā Kobo e-lasītāji, nepieciešams lejupielādēt failu un pārsūtīt to uz savu ierīci. Izpildiet palīdzības centrā sniegtos detalizētos norādījumus, lai pārsūtītu failus uz atbalstītiem e-lasītājiem.