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dc.contributor.authorNegri, Sergio
dc.contributor.authorBelanche Muñoz, Luis Antonio
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Ciències de la Computació
dc.date.accessioned2020-03-30T12:11:26Z
dc.date.available2020-03-30T12:11:26Z
dc.date.issued2001
dc.identifier.citationNegri, S.; Belanche, L. Heterogeneous Kohonen networks. A: International Work-Conference on Artificial and Natural Neural Network. "Connectionist models of neurons, learning processes, and artificial intelligence: 6th International Work-Conference on Artificial and Natural Neural Networks, IWANN 2001: Granada, Spain, June 13-15, 2001: proceedings, part 1". Berlín: Springer, 2001, p. 243-252.
dc.identifier.isbn978-3-540-45720-6
dc.identifier.urihttp://hdl.handle.net/2117/182259
dc.description.abstractA large number of practical problems involves elements that are described as a mixture of qualitative and quantitative infomation, and whose description is probably incomplete. The self-organizing map is an effective tool for visualization of high-dimensional continuous data. In this work, we extend the network and training algorithm to cope with heterogeneous information, as well as missing values. The classification performance on a collection of benchmarking data sets is compared in different configurations. Various visualization methods are suggested to aid users interpret post-training results.
dc.format.extent10 p.
dc.language.isoeng
dc.publisherSpringer
dc.subjectÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
dc.subject.lcshMachine learning
dc.subject.lcshInformation visualization
dc.subject.lcshSelf-organizing maps
dc.subject.otherHeterogeneous Kohonen networks
dc.subject.otherQualitative information
dc.subject.otherQuantitative information
dc.subject.otherHigh-dimensional continuous data visualization
dc.subject.otherTraining algorithm
dc.subject.otherHeterogeneous information
dc.subject.otherClassification performance
dc.titleHeterogeneous Kohonen networks
dc.typeConference report
dc.subject.lemacAprenentatge automàtic
dc.subject.lemacVisualització de la informació
dc.contributor.groupUniversitat Politècnica de Catalunya. SOCO - Soft Computing
dc.identifier.doi10.1007/3-540-45720-8_28
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://link.springer.com/chapter/10.1007/3-540-45720-8_28
dc.rights.accessOpen Access
local.identifier.drac27657687
dc.description.versionPostprint (author's final draft)
local.citation.authorNegri, S.; Belanche, Ll.
local.citation.contributorInternational Work-Conference on Artificial and Natural Neural Network
local.citation.pubplaceBerlín
local.citation.publicationNameConnectionist models of neurons, learning processes, and artificial intelligence: 6th International Work-Conference on Artificial and Natural Neural Networks, IWANN 2001: Granada, Spain, June 13-15, 2001: proceedings, part 1
local.citation.startingPage243
local.citation.endingPage252


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