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Reference TypeConference Proceedings
Identifier8JMKD3MGPBW34M/386A2NP
Repositorysid.inpe.br/sibgrapi/2010/08.28.22.02
Metadatasid.inpe.br/sibgrapi/2010/08.28.22.02.12
Sitesibgrapi.sid.inpe.br
Citation KeyRochaPapaMeir:2010:HoFaYo
Author1 Rocha, Anderson
2 Papa, João Paulo
3 Meira, Luis A. A.
Affiliation1 Institute of Computing, University of Campinas (UNICAMP), Brazil
2 Department of Computer Science, State University of São Paulo (UNESP), Brazil
3 Department of Science and Technology, Federal University of São Paulo (UNIFESP), Brazil
TitleHow Far You Can Get Using Machine Learning Black-Boxes
Conference NameConference on Graphics, Patterns and Images, 23 (SIBGRAPI)
Year2010
EditorBellon, Olga
Esperança, Claudio
Book TitleProceedings
DateAug. 30 - Sep. 3, 2010
Publisher CityLos Alamitos
PublisherIEEE Computer Society
Conference LocationGramado
KeywordsLearning Black-Boxes, Metrics Space, Pattern Analysis, Support Vector Machines, Optimum-Path Forest, Neural Networks, K-Nearest Neighbors.
AbstractSupervised Learning (SL) is a machine learning research area which aims at developing techniques able to take advantage from labeled training samples to make decisions over unseen examples. Recently, a lot of tools have been presented in order to perform machine learning in a more straightfor- ward and transparent manner. However, one problem that is increasingly present in most of the SL problems being solved is that, sometimes, researchers do not completely understand what supervised learning is and, more often than not, publish results using machine learning black-boxes. In this paper, we shed light over the use of machine learning black-boxes and show researchers how far they can get using these out-of-the- box solutions instead of going deeper into the machinery of the classifiers. Here, we focus on one aspect of classifiers namely the way they compare examples in the feature space and show how a simple knowledge about the classifiers machinery can lift the results way beyond out-of-the-box machine learning solutions.
Languageen
Tertiary TypeFull Paper
FormatPrinted, On-line.
Size305 KiB
Number of Files1
Target Filerocha-et-al-sibgrapi-2010-camera-ready.pdf
Last Update2010:08.28.22.02.12 sid.inpe.br/banon/2001/03.30.15.38 anderson.rocha@ic.unicamp.br
Metadata Last Update2010:10.01.04.19.37 sid.inpe.br/banon/2001/03.30.15.38 anderson.rocha@ic.unicamp.br {D 2010}
Document Stagecompleted
Is the master or a copy?is the master
Mirrordpi.inpe.br/banon-pc2@80/2006/08.30.19.27
e-Mail Addressanderson.rocha@ic.unicamp.br
User Groupanderson.rocha@ic.unicamp.br
Visibilityshown
Transferable1
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
Content TypeExternal Contribution
source Directory Contentthere are no files
agreement Directory Contentthere are no files
History2010-10-01 04:19:37 :: anderson.rocha@ic.unicamp.br -> :: 2010
Empty Fieldsaccessionnumber archivingpolicy archivist area callnumber copyholder copyright creatorhistory descriptionlevel dissemination documentstage doi edition electronicmailaddress group holdercode isbn issn label lineage mark nextedition nexthigherunit notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url versiontype volume
Access Date2020, Nov. 24

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