Close

1. Identity statement
Reference TypeJournal Article
Sitemtc-m16b.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identifier6qtX3pFwXQZGivnK2Y/NmDG3
Repositorysid.inpe.br/mtc-m17@80/2006/12.04.13.25   (restricted access)
Last Update2007:06.28.13.14.15 (UTC) administrator
Metadata Repositorysid.inpe.br/mtc-m17@80/2006/12.04.13.25.19
Metadata Last Update2018:06.05.03.33.55 (UTC) administrator
Secondary KeyINPE-14762-PRE/9733
ISSN0169-023X
Citation KeySantanaFrRoCaViReCo:2007:StImMo
TitleStrategies for improving the modeling and interpretability of Bayesian networks
Year2007
MonthOct.
Access Date2024, Apr. 28
Secondary TypePRE PI
Number of Files1
Size447 KiB
2. Context
Author1 Santana, Ádamo L.
2 Francês, Carlos R.
3 Rocha, Cláudio A.
4 Carvalho, Solon Venâncio de
5 Vijaykumar, Nandamudi Lankalapalli
6 Rego, Liviane P.
7 Costa, João C.
Resume Identifier1
2
3
4
5 8JMKD3MGP5W/3C9JHTU
Group1
2
3
4 LAC-INPE-MCT-BR
5 LAC-INPE-MCT-BR
Affiliation1 UFPA
2 UFPA
3 Universidade da Amazônia
4 Instituto Nacional de Pesquisas Espaciais (INPE)
5 Instituto Nacional de Pesquisas Espaciais (INPE)
6 UFPA
7 UFPA
JournalData and Knowledge Engineering
Volume63
Number1
Pages91-107
History (UTC)2006-12-04 13:25:54 :: simone -> administrator ::
2007-04-03 01:32:35 :: administrator -> simone ::
2007-06-28 13:14:15 :: simone -> administrator ::
2012-11-24 01:39:25 :: administrator -> simone :: 2007
2013-02-20 15:19:52 :: simone -> administrator :: 2007
2018-06-05 03:33:55 :: administrator -> marciana :: 2007
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
KeywordsKnowledge discovery
Markov chains
Bayesian networks
Multivariate regression
AbstractOne of the main factors for the knowledge discovery success is related to the comprehensibility of the patterns discovered by applying data mining techniques. Amongst which we can point out the Bayesian networks as one of the most prominent when considering the easiness of knowledge interpretation achieved. Bayesian networks, however, present limitations and disadvantages regarding their use and applicability. This paper presents an extension for the improvement of Bayesian networks, treating aspects such as performance, as well as interpretability and use of their results; incorporating genetic algorithms in the model, multivariate regression for structure learning and temporal aspects using Markov chains.
AreaCOMP
Arrangementurlib.net > BDMCI > Fonds > Produção anterior à 2021 > LABAC > Strategies for improving...
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Contentthere are no files
4. Conditions of access and use
Languageen
Target Filestrategies for improving.pdf
User Groupadministrator
simone
Visibilityshown
Copy HolderSID/SCD
Archiving Policydenypublisher denyfinaldraft24
Read Permissiondeny from all and allow from 150.163
5. Allied materials
Next Higher Units8JMKD3MGPCW/3ESGTTP
DisseminationPORTALCAPES
Host Collectionlcp.inpe.br/ignes/2004/02.12.18.39
cptec.inpe.br/walmeida/2003/04.25.17.12
6. Notes
Empty Fieldsalternatejournal archivist callnumber copyright creatorhistory descriptionlevel documentstage doi e-mailaddress electronicmailaddress format isbn label lineage mark mirrorrepository nextedition notes orcid parameterlist parentrepositories previousedition previouslowerunit progress project readergroup rightsholder schedulinginformation secondarydate secondarymark session shorttitle sponsor subject tertiarymark tertiarytype typeofwork url versiontype
7. Description control
e-Mail (login)marciana
update 


Close