Close

1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier6qtX3pFwXQZG2LgkFdY/URfSG
Repositorysid.inpe.br/sibgrapi@80/2008/07.23.17.43
Last Update2008:09.23.13.10.04 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi@80/2008/07.23.17.43.49
Metadata Last Update2022:06.14.00.13.50 (UTC) administrator
DOI10.1109/SIBGRAPI.2008.35
Citation KeyRibeiroHash:2008:NeTrAl
TitleA New Training Algorithm for Pattern Recognition Technique Based on Straight Line Segments
FormatPrinted, On-line.
Year2008
Access Date2024, May 02
Number of Files1
Size1077 KiB
2. Context
Author1 Ribeiro, Joao Henrique Burckas
2 Hashimoto, Ronaldo Fumio
Affiliation1 Institute of Mathematics and Statistics - University of Sao Paulo
2 Institute of Mathematics and Statistics - University of Sao Paulo
EditorJung, Cláudio Rosito
Walter, Marcelo
e-Mail Addressronaldo@ime.usp.br
Conference NameBrazilian Symposium on Computer Graphics and Image Processing, 21 (SIBGRAPI)
Conference LocationCampo Grande, MS, Brazil
Date12-15 Oct. 2008
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2008-09-23 13:10:04 :: ronaldo@ime.usp.br -> administrator ::
2009-08-13 20:39:01 :: administrator -> ronaldo@ime.usp.br ::
2010-08-28 20:03:23 :: ronaldo@ime.usp.br -> administrator ::
2022-06-14 00:13:50 :: administrator -> :: 2008
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
KeywordsStraight Line Segments
Machine Learning
Pattern Recognition
Classification
Support Vector Machine
AbstractRecently, a new Pattern Recognition technique based on straight line segments (SLSs) was presented. The key issue in this new technique is to find a function based on distances between points and two sets of SLSs that minimizes a certain error or risk criterion. An algorithm for solving this optimization problem is called training algorithm. Although this technique seems to be very promising, the first presented training algorithm is based on a heuristic. In fact, the search for this best function is a hard nonlinear optimization problem. In this paper, we present a new and improved training algorithm for the SLS technique based on gradient descent optimization method. We have applied this new training algorithm to artificial and public data sets and their results confirm the improvement of this methodology..
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2008 > A New Training...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > A New Training...
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Contentthere are no files
4. Conditions of access and use
data URLhttp://urlib.net/ibi/6qtX3pFwXQZG2LgkFdY/URfSG
zipped data URLhttp://urlib.net/zip/6qtX3pFwXQZG2LgkFdY/URfSG
Languageen
Target Filesibgrapi2008_sls.pdf
User Groupronaldo@ime.usp.br
administrator
Visibilityshown
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPEW34M/46SG4TH
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2022/05.14.04.55 2
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination documentstage edition electronicmailaddress group isbn issn label lineage mark 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


Close