Neural PCA and Maximum Likelihood Hebbian Learning on the GPU

Krömer, Pavel; Corchado Rodríguez, Emilio Santiago; Snášel, Václav; Platos, Jan; García Hernández, Laura

Título

dc.contributor.author	Krömer, Pavel
dc.contributor.author	Corchado Rodríguez, Emilio Santiago
dc.contributor.author	Snášel, Václav
dc.contributor.author	Platos, Jan
dc.contributor.author	García Hernández, Laura
dc.date.accessioned	2017-09-06T09:14:25Z
dc.date.available	2017-09-06T09:14:25Z
dc.date.issued	2012
dc.identifier.citation	Artificial Neural Networks and Machine Learning – ICANN 2012 Lecture Notes in Computer Science. 22nd International Conference on Artificial Neural Networks, Lausanne, Switzerland, September 11-14, 2012, Proceedings, Part II. Lecture Notes in Computer Science. Volumen 7553, pp. 132-139.
dc.identifier.isbn	978-3-642-33265-4 (Print) / 978-3-642-33266-1 (Online)
dc.identifier.issn	0302-9743 (Print) / 1611-3349 (Online)
dc.identifier.uri	http://hdl.handle.net/10366/134874
dc.description.abstract	This study introduces a novel fine-grained parallel implementation of a neural principal component analysis (neural PCA) variant and the maximum Likelihood Hebbian Learning (MLHL) network designed for modern many-core graphics processing units (GPUs). The parallel implementation as well as the computational experiments conducted in order to evaluate the speedup achieved by the GPU are presented and discussed. The evaluation was done on a well-known artificial data set, the 2D bars data set.
dc.format.mimetype	application/pdf
dc.language.iso	en
dc.publisher	Springer Science + Business Media
dc.rights	Attribution-NonCommercial-NoDerivs 3.0 Unported
dc.rights.uri	https://creativecommons.org/licenses/by-nc-nd/3.0/
dc.subject	Computer Science
dc.title	Neural PCA and Maximum Likelihood Hebbian Learning on the GPU
dc.type	info:eu-repo/semantics/conferenceObject
dc.rights.accessRights	info:eu-repo/semantics/openAccess