| 1. Identity statement | |
| Reference Type | Conference Paper (Conference Proceedings) |
| Site | sibgrapi.sid.inpe.br |
| Holder Code | ibi 8JMKD3MGPEW34M/46T9EHH |
| Identifier | 8JMKD3MGPBW34M/386A2NP |
| Repository | sid.inpe.br/sibgrapi/2010/08.28.22.02 |
| Last Update | 2010:08.28.22.02.12 (UTC) administrator |
| Metadata Repository | sid.inpe.br/sibgrapi/2010/08.28.22.02.12 |
| Metadata Last Update | 2022:06.14.00.06.52 (UTC) administrator |
| DOI | 10.1109/SIBGRAPI.2010.34 |
| Citation Key | RochaPapaMeir:2010:HoFaYo |
| Title | How Far You Can Get Using Machine Learning Black-Boxes  |
| Format | Printed, On-line. |
| Year | 2010 |
| Access Date | 2024, Apr. 29 |
| Number of Files | 1 |
| Size | 305 KiB |
| 2. Context | |
| Author | 1 Rocha, Anderson 2 Papa, João Paulo 3 Meira, Luis A. A. |
| Affiliation | 1 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 |
| Editor | Bellon, Olga Esperança, Claudio |
| e-Mail Address | anderson.rocha@ic.unicamp.br |
| Conference Name | Conference on Graphics, Patterns and Images, 23 (SIBGRAPI) |
| Conference Location | Gramado, RS, Brazil |
| Date | 30 Aug.-3 Sep. 2010 |
| Publisher | IEEE Computer Society |
| Publisher City | Los Alamitos |
| Book Title | Proceedings |
| Tertiary Type | Full Paper |
| History (UTC) | 2010-10-01 04:19:37 :: anderson.rocha@ic.unicamp.br -> administrator :: 2010 2022-06-14 00:06:52 :: administrator -> :: 2010 |
| 3. Content and structure | |
| Is the master or a copy? | is the master |
| Content Stage | completed |
| Transferable | 1 |
| Version Type | finaldraft |
| Keywords | Learning Black-Boxes Metrics Space Pattern Analysis Support Vector Machines Optimum-Path Forest Neural Networks K-Nearest Neighbors |
| Abstract | Supervised 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. |
| Arrangement 1 | urlib.net > SDLA > Fonds > SIBGRAPI 2010 > How Far You... |
| Arrangement 2 | urlib.net > SDLA > Fonds > Full Index > How Far You... |
| doc Directory Content | access |
| source Directory Content | there are no files |
| agreement Directory Content | there are no files |
| 4. Conditions of access and use | |
| data URL | http://urlib.net/ibi/8JMKD3MGPBW34M/386A2NP |
| zipped data URL | http://urlib.net/zip/8JMKD3MGPBW34M/386A2NP |
| Language | en |
| Target File | rocha-et-al-sibgrapi-2010-camera-ready.pdf |
| User Group | anderson.rocha@ic.unicamp.br |
| Visibility | shown |
| 5. Allied materials | |
| Next Higher Units | 8JMKD3MGPEW34M/46SJT6B 8JMKD3MGPEW34M/4742MCS |
| Citing Item List | sid.inpe.br/sibgrapi/2022/05.14.20.21 4 |
| Host Collection | sid.inpe.br/banon/2001/03.30.15.38 |
| 6. Notes | |
| Empty Fields | archivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination documentstage edition electronicmailaddress group isbn issn label lineage mark mirrorrepository nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url volume |