Unsupervised learning via self-organization: a dynamic approach

To aid in intelligent data mining, this book introduces a new family of unsupervised algorithms that have a basis in self-organization, yet are free from many of the constraints typical of other well known self-organizing architectures. It then moves through a series of pertinent real world applicat...

Descripción completa

Detalles Bibliográficos
Autores principales: Kyan, Matthew, Muneesawang, Paisarn, Jarrah, Kambiz, Guan, Ling
Lenguaje:eng
Publicado: Wiley-IEEE Press 2014
Materias:
Computing and Computers
Acceso en línea:http://cds.cern.ch/record/1748075
_version_ 1780942974567317504
author Kyan, Matthew
Muneesawang, Paisarn
Jarrah, Kambiz
Guan, Ling
author_facet Kyan, Matthew
Muneesawang, Paisarn
Jarrah, Kambiz
Guan, Ling
author_sort Kyan, Matthew
collection CERN
description To aid in intelligent data mining, this book introduces a new family of unsupervised algorithms that have a basis in self-organization, yet are free from many of the constraints typical of other well known self-organizing architectures. It then moves through a series of pertinent real world applications with regards to the processing of multimedia data from its role in generic image processing techniques such as the automated modeling and removal of impulse noise in digital images, to problems in digital asset management, and its various roles in feature extraction, visual enhancement, segmentation, and analysis of microbiological image data.
id cern-1748075
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2014
publisher Wiley-IEEE Press
record_format invenio
spelling cern-17480752021-04-21T20:55:40Zhttp://cds.cern.ch/record/1748075engKyan, MatthewMuneesawang, PaisarnJarrah, KambizGuan, LingUnsupervised learning via self-organization: a dynamic approachComputing and ComputersTo aid in intelligent data mining, this book introduces a new family of unsupervised algorithms that have a basis in self-organization, yet are free from many of the constraints typical of other well known self-organizing architectures. It then moves through a series of pertinent real world applications with regards to the processing of multimedia data from its role in generic image processing techniques such as the automated modeling and removal of impulse noise in digital images, to problems in digital asset management, and its various roles in feature extraction, visual enhancement, segmentation, and analysis of microbiological image data.Wiley-IEEE Pressoai:cds.cern.ch:17480752014
spellingShingle Computing and Computers
Kyan, Matthew
Muneesawang, Paisarn
Jarrah, Kambiz
Guan, Ling
Unsupervised learning via self-organization: a dynamic approach
title Unsupervised learning via self-organization: a dynamic approach
title_full Unsupervised learning via self-organization: a dynamic approach
title_fullStr Unsupervised learning via self-organization: a dynamic approach
title_full_unstemmed Unsupervised learning via self-organization: a dynamic approach
title_short Unsupervised learning via self-organization: a dynamic approach
title_sort unsupervised learning via self-organization: a dynamic approach
topic Computing and Computers
url http://cds.cern.ch/record/1748075
work_keys_str_mv AT kyanmatthew unsupervisedlearningviaselforganizationadynamicapproach
AT muneesawangpaisarn unsupervisedlearningviaselforganizationadynamicapproach
AT jarrahkambiz unsupervisedlearningviaselforganizationadynamicapproach
AT guanling unsupervisedlearningviaselforganizationadynamicapproach

Ejemplares similares