| 1. Identity statement | |
| Reference Type | Conference Paper (Conference Proceedings) |
| Site | sibgrapi.sid.inpe.br |
| Holder Code | ibi 8JMKD3MGPEW34M/46T9EHH |
| Identifier | 8JMKD3MGPBW4/35S5CTE |
| Repository | sid.inpe.br/sibgrapi@80/2009/08.17.15.47 |
| Last Update | 2009:08.17.15.47.31 (UTC) administrator |
| Metadata Repository | sid.inpe.br/sibgrapi@80/2009/08.17.15.47.33 |
| Metadata Last Update | 2022:06.14.00.13.56 (UTC) administrator |
| DOI | 10.1109/SIBGRAPI.2009.20 |
| Citation Key | LageCaPeBoTaLeLo:2009:SuVeLe |
| Title | Support Vectors Learning for Vector Field Reconstruction  |
| Format | Printed, On-line. |
| Year | 2009 |
| Access Date | 2024, Apr. 29 |
| Number of Files | 1 |
| Size | 5554 KiB |
| 2. Context | |
| Author | 1 Lage, Marcos 2 Castro, Rener 3 Petronetto, Fabiano 4 Bordignon, Alex 5 Tavares, Geovan 6 Lewiner, Thomas 7 Lopes, Hélio |
| Affiliation | 1 Matmídia Laboratory – Department of Mathematics, PUC–Rio – Rio de Janeiro, Brazil 2 . 3 Matmídia Laboratory – Department of Mathematics, PUC–Rio – Rio de Janeiro, Brazil 4 Matmídia Laboratory – Department of Mathematics, PUC–Rio – Rio de Janeiro, Brazil 5 Matmídia Laboratory – Department of Mathematics, PUC–Rio – Rio de Janeiro, Brazil 6 Matmídia Laboratory – Department of Mathematics, PUC–Rio – Rio de Janeiro, Brazil 7 Matmídia Laboratory – Department of Mathematics, PUC–Rio – Rio de Janeiro, Brazil |
| Editor | Nonato, Luis Gustavo Scharcanski, Jacob |
| e-Mail Address | lewiner@gmail.com |
| Conference Name | Brazilian Symposium on Computer Graphics and Image Processing, 22 (SIBGRAPI) |
| Conference Location | Rio de Janeiro, RJ, Brazil |
| Date | 11-14 Oct. 2009 |
| Publisher | IEEE Computer Society |
| Publisher City | Los Alamitos |
| Book Title | Proceedings |
| Tertiary Type | Full Paper |
| History (UTC) | 2010-08-28 20:03:25 :: lewiner@gmail.com -> administrator :: 2022-06-14 00:13:56 :: administrator -> :: 2009 |
| 3. Content and structure | |
| Is the master or a copy? | is the master |
| Content Stage | completed |
| Transferable | 1 |
| Version Type | finaldraft |
| Keywords | Vector Field Support Vector Machine |
| Abstract | Sampled vector fields generally appear as measurements of real phenomena. They can be obtained by the use of a Particle Image Velocimetry acquisition device, or as the result of a physical simulation, such as a fluid flow simulation, among many examples. This paper proposes to formulate the unstructured vector field reconstruction and approximation through Machine-Learning. The machine learns from the samples a global vector field estimation function that could be evaluated at arbitrary points from the whole domain. Using an adaptation of the Support Vector Regression method for multi-scale analysis, the proposed method provides a global, analytical expression for the reconstructed vector field through an efficient non-linear optimization. Experiments on artificial and real data show a statistically robust behavior of the proposed technique. |
| Arrangement 1 | urlib.net > SDLA > Fonds > SIBGRAPI 2009 > Support Vectors Learning... |
| Arrangement 2 | urlib.net > SDLA > Fonds > Full Index > Support Vectors Learning... |
| 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/8JMKD3MGPBW4/35S5CTE |
| zipped data URL | http://urlib.net/zip/8JMKD3MGPBW4/35S5CTE |
| Language | en |
| Target File | 57787_2.pdf |
| User Group | lewiner@gmail.com |
| Visibility | shown |
| 5. Allied materials | |
| Next Higher Units | 8JMKD3MGPEW34M/46SJQ2S 8JMKD3MGPEW34M/4742MCS |
| Citing Item List | sid.inpe.br/sibgrapi/2022/05.14.19.43 3 |
| 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 |