Restricted Boltzmann Machines
Charu Aggarwal
Additional contact information
Charu Aggarwal: International Business Machines, IBM T. J. Watson Research Center
Chapter Chapter 7 in Neural Networks and Deep Learning, 2023, pp 231-264 from Springer
Abstract:
Abstract The restricted Boltzmann machine (RBM) is a fundamentally different model from the feed-forward network. Conventional neural networks are input-output mapping networks where a set of inputs is mapped to a set of outputs. On the other hand, RBMs are networks in which the probabilistic states of a network are learned for a set of inputs, which is useful for unsupervised modeling.
Date: 2023
References: Add references at CitEc
Citations:
There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-29642-0_7
Ordering information: This item can be ordered from
http://www.springer.com/9783031296420
DOI: 10.1007/978-3-031-29642-0_7
Access Statistics for this chapter
More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().