Probabilistic neural networks: A brief overview of theory, implementation, and application

Mohebali, B; Tahmassebi, A; Meyer-Baese, A; Gandomi, AH

Probabilistic neural networks: A brief overview of theory, implementation, and application

Mohebali, B Tahmassebi, A Meyer-Baese, A Gandomi, AH

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Publisher:: Elsevier
Publication Type:: Chapter
Citation:: Handbook of Probabilistic Models, 2020, pp. 347-367
Issue Date:: 2020

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Field	Value	Language
dc.contributor.author	Mohebali, B
dc.contributor.author	Tahmassebi, A
dc.contributor.author	Meyer-Baese, A
dc.contributor.author	Gandomi, AH
dc.date.accessioned	2021-02-02T04:53:30Z
dc.date.available	2021-02-02T04:53:30Z
dc.date.issued	2020
dc.identifier.citation	Handbook of Probabilistic Models, 2020, pp. 347-367
dc.identifier.isbn	9780128165461
dc.identifier.uri	http://hdl.handle.net/10453/145733
dc.description.abstract	© 2020 Elsevier Inc. All rights reserved. Probabilistic neural networks (PNNs) offer a scalable alternative to the conventional back-propagation neural networks in classification problems without the need for massive forward and backward calculations that is associated with the ordinary neural networks. In addition, they can work with smaller sets of training data. However, this advantage may come at a cost of requiring large amounts of memory as the training data get larger. This chapter takes a look at the fundamental mathematics behind the modern PNNs, their application, and approaches that address some practical issues that come with them.
dc.language	en
dc.publisher	Elsevier
dc.relation.ispartof	Handbook of Probabilistic Models
dc.relation.isbasedon	10.1016/B978-0-12-816514-0.00014-X
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Probabilistic neural networks: A brief overview of theory, implementation, and application
dc.type	Chapter
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/A/DRsch The Data Science Institute
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2021-02-02T04:53:20Z
pubs.place-of-publication	The Netherlands
pubs.publication-status	Published
dc.location	The Netherlands

Abstract:

© 2020 Elsevier Inc. All rights reserved. Probabilistic neural networks (PNNs) offer a scalable alternative to the conventional back-propagation neural networks in classification problems without the need for massive forward and backward calculations that is associated with the ordinary neural networks. In addition, they can work with smaller sets of training data. However, this advantage may come at a cost of requiring large amounts of memory as the training data get larger. This chapter takes a look at the fundamental mathematics behind the modern PNNs, their application, and approaches that address some practical issues that come with them.

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http://hdl.handle.net/10453/145733