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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPBW34M/3A3H72S
Repositorysid.inpe.br/sibgrapi/2011/07.10.05.55
Last Update2011:07.10.05.55.07 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2011/07.10.05.55.06
Metadata Last Update2022:06.14.00.07.14 (UTC) administrator
DOI10.1109/SIBGRAPI.2011.8
Citation KeyMedinaRodriguezHash:2011:CoDiOp
TitleCombining Dialectical Optimization and Gradient Descent Methods for Improving the Accuracy of Straight Line Segment Classifiers
FormatDVD, On-line.
Year2011
Access Date2024, Sep. 17
Number of Files1
Size1220 KiB
2. Context
Author1 Medina Rodriguez, Rosario A.
2 Hashimoto, Ronaldo Fumio
Affiliation1 University of Sao Paulo
2 University of Sao Paulo
EditorLewiner, Thomas
Torres, Ricardo
e-Mail Addressrosarior@ime.usp.br
Conference NameConference on Graphics, Patterns and Images, 24 (SIBGRAPI)
Conference LocationMaceió, AL, Brazil
Date28-31 Aug. 2011
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2011-07-23 15:36:12 :: rosarior@ime.usp.br -> administrator :: 2011
2022-06-14 00:07:14 :: administrator -> :: 2011
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
Keywordsstraight line segments
gradient descent technique
dialectical optimization
genetic algorithms
pattern recognition
AbstractA recent published pattern recognition technique called Straight Line Segment (SLS) uses two sets of straight line segments to classify a set of points from two different classes and it is based on distances between these points and each set of straight line segments. It has been demonstrated that, using this technique, it is possible to generate classifiers which can reach high accuracy rates for supervised pattern classification. However, a critical issue in this technique is to find the optimal positions of the straight line segments given a training data set. This paper proposes a combining method of the dialectical optimization method (DOM) and the gradient descent technique for solving this optimization problem. The main advantage of DOM, such as any evolutionary algorithm, is the capability of escaping from local optimum by multi-point stochastic searching. On the other hand, the strength of gradient descent method is the ability of finding local optimum by pointing the direction that maximizes the objective function. Our hybrid method combines the main characteristics of these two methods. We have applied our combining approach to several data sets obtained from artificial distributions and UCI databases. These experiments show that the proposed algorithm in most cases has higher classification rates with respect to single gradient descent method and the combination of gradient descent with genetic algorithms.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2011 > Combining Dialectical Optimization...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > Combining Dialectical Optimization...
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPBW34M/3A3H72S
zipped data URLhttp://urlib.net/zip/8JMKD3MGPBW34M/3A3H72S
Languageen
Target FileCombining Dialectical Optimization and Gradient Descent Methods for Improving the Accuracy of Straight Line Segment Classifiers.pdf
User Grouprosarior@ime.usp.br
Visibilityshown
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPEW34M/46SKNPE
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2022/05.15.00.56 20
sid.inpe.br/sibgrapi/2022/06.10.21.49 1
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


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