Offline Recognition of Handwritten Numeral Characters with Polynomial Neural Networks Using Topological Features
Identifieur interne : 001946 ( Istex/Corpus ); précédent : 001945; suivant : 001947Offline Recognition of Handwritten Numeral Characters with Polynomial Neural Networks Using Topological Features
Auteurs : M. El-AlfySource :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2010.
Abstract
Abstract: Group-Method of Data Handling (GMDH) has been recognized as a powerful tool in machine learning. It has the potential to build predictive neural network models of polynomial functions using only a reduced set of features which minimizes the prediction error. This paper explores the offline recognition of isolated handwritten numeral characters described with non-Gaussian topological features using GMDH-based polynomial networks. In order to study the effectiveness of the proposed approach, we apply it on a publicly available dataset of isolated handwritten numerals and compare the results with five other state-of-the-art classifiers: multilayer Perceptron, support-vector machine, radial-basis function, naïve Bayes and rule-based classifiers. In addition to improving the classification accuracy and the per-class performance measures, using GMDH-based polynomial neural networks has led to significant feature dimensionality reduction.
Url:
DOI: 10.1007/978-3-642-13059-5_18
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It has the potential to build predictive neural network models of polynomial functions using only a reduced set of features which minimizes the prediction error. This paper explores the offline recognition of isolated handwritten numeral characters described with non-Gaussian topological features using GMDH-based polynomial networks. In order to study the effectiveness of the proposed approach, we apply it on a publicly available dataset of isolated handwritten numerals and compare the results with five other state-of-the-art classifiers: multilayer Perceptron, support-vector machine, radial-basis function, naïve Bayes and rule-based classifiers. In addition to improving the classification accuracy and the per-class performance measures, using GMDH-based polynomial neural networks has led to significant feature dimensionality reduction.</div></front></TEI><istex><corpusName>springer</corpusName><author><json:item><name>El-Sayed M. 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It has the potential to build predictive neural network models of polynomial functions using only a reduced set of features which minimizes the prediction error. This paper explores the offline recognition of isolated handwritten numeral characters described with non-Gaussian topological features using GMDH-based polynomial networks. In order to study the effectiveness of the proposed approach, we apply it on a publicly available dataset of isolated handwritten numerals and compare the results with five other state-of-the-art classifiers: multilayer Perceptron, support-vector machine, radial-basis function, naïve Bayes and rule-based classifiers. In addition to improving the classification accuracy and the per-class performance measures, using GMDH-based polynomial neural networks has led to significant feature dimensionality reduction.</p></abstract><textClass><keywords scheme="Book Subject Collection"><list><label>SUCO11645</label><item><term>Computer Science</term></item></list></keywords></textClass><textClass><keywords scheme="Book Subject Group"><list><label>I</label><label>I21017</label><label>I2203X</label><label>I21041</label><label>I18040</label><label>I18032</label><label>I18030</label><item><term>Computer Science</term></item><item><term>Artificial Intelligence (incl. 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University</OrgName><OrgAddress><City>Stanford</City><State>CA</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff7"><OrgName>Weizmann Institute of Science</OrgName><OrgAddress><City>Rehovot</City><Country>Israel</Country></OrgAddress></Affiliation><Affiliation ID="Aff8"><OrgName>University of Bern</OrgName><OrgAddress><City>Bern</City><Country>Switzerland</Country></OrgAddress></Affiliation><Affiliation ID="Aff9"><OrgName>Indian Institute of Technology</OrgName><OrgAddress><City>Madras</City><Country>India</Country></OrgAddress></Affiliation><Affiliation ID="Aff10"><OrgName>University of Dortmund</OrgName><OrgAddress><City>Dortmund</City><Country>Germany</Country></OrgAddress></Affiliation><Affiliation ID="Aff11"><OrgName>Massachusetts Institute of Technology</OrgName><OrgAddress><State>MA</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff12"><OrgName>University of California</OrgName><OrgAddress><City>Los Angeles</City><State>CA</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff13"><OrgName>University of California</OrgName><OrgAddress><City>Berkeley</City><State>CA</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff14"><OrgName>Rice University</OrgName><OrgAddress><City>Houston</City><State>TX</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff15"><OrgName>Max-Planck Institute of Computer Science</OrgName><OrgAddress><City>Saarbrücken</City><Country>Germany</Country></OrgAddress></Affiliation></EditorGroup></SeriesHeader><SubSeries><SubSeriesInfo><SubSeriesID>1244</SubSeriesID><SubSeriesPrintISSN>0302-9743</SubSeriesPrintISSN><SubSeriesElectronicISSN>1611-3349</SubSeriesElectronicISSN><SubSeriesTitle Language="En">Lecture Notes in Artificial Intelligence</SubSeriesTitle></SubSeriesInfo><SubSeriesHeader><EditorGroup><Editor AffiliationIDS="Aff16"><EditorName DisplayOrder="Western"><GivenName>Randy</GivenName><FamilyName>Goebel</FamilyName></EditorName></Editor><Editor AffiliationIDS="Aff17"><EditorName DisplayOrder="Western"><GivenName>Jörg</GivenName><FamilyName>Siekmann</FamilyName></EditorName></Editor><Editor AffiliationIDS="Aff18"><EditorName DisplayOrder="Western"><GivenName>Wolfgang</GivenName><FamilyName>Wahlster</FamilyName></EditorName></Editor><Affiliation ID="Aff16"><OrgName>University of Alberta</OrgName><OrgAddress><City>Edmonton</City><Country>Canada</Country></OrgAddress></Affiliation><Affiliation ID="Aff17"><OrgName>University of Saarland</OrgName><OrgAddress><City>Saarbrücken</City><Country>Germany</Country></OrgAddress></Affiliation><Affiliation ID="Aff18"><OrgName>DFKI and University of Saarland</OrgName><OrgAddress><City>Saarbrücken</City><Country>Germany</Country></OrgAddress></Affiliation></EditorGroup></SubSeriesHeader></SubSeries><Book Language="En"><BookInfo BookProductType="Proceedings" ContainsESM="No" Language="En" MediaType="eBook" NumberingDepth="2" NumberingStyle="ContentOnly" OutputMedium="All" TocLevels="0"><BookID>978-3-642-13059-5</BookID><BookTitle>Advances in Artificial Intelligence</BookTitle><BookSubTitle>23rd Canadian Conference on Artificial Intelligence, Canadian AI 2010, Ottawa, Canada, May 31 – June 2, 2010. Proceedings</BookSubTitle><BookVolumeNumber>6085</BookVolumeNumber><BookSequenceNumber>6085</BookSequenceNumber><BookDOI>10.1007/978-3-642-13059-5</BookDOI><BookTitleID>211296</BookTitleID><BookPrintISBN>978-3-642-13058-8</BookPrintISBN><BookElectronicISBN>978-3-642-13059-5</BookElectronicISBN><BookChapterCount>63</BookChapterCount><BookCopyright><CopyrightHolderName>Springer-Verlag Berlin Heidelberg</CopyrightHolderName><CopyrightYear>2010</CopyrightYear></BookCopyright><BookSubjectGroup><BookSubject Code="I" Type="Primary">Computer Science</BookSubject><BookSubject Code="I21017" Priority="1" Type="Secondary">Artificial Intelligence (incl. Robotics)</BookSubject><BookSubject Code="I2203X" Priority="2" Type="Secondary">Pattern Recognition</BookSubject><BookSubject Code="I21041" Priority="3" Type="Secondary">Language Translation and Linguistics</BookSubject><BookSubject Code="I18040" Priority="4" Type="Secondary">Information Systems Applications (incl.Internet)</BookSubject><BookSubject Code="I18032" Priority="5" Type="Secondary">Information Storage and Retrieval</BookSubject><BookSubject Code="I18030" Priority="6" Type="Secondary">Data Mining and Knowledge Discovery</BookSubject><SubjectCollection Code="SUCO11645">Computer Science</SubjectCollection></BookSubjectGroup><BookContext><SeriesID>558</SeriesID><SubSeriesID>1244</SubSeriesID></BookContext></BookInfo><BookHeader><EditorGroup><Editor AffiliationIDS="Aff19"><EditorName DisplayOrder="Western"><GivenName>Atefeh</GivenName><FamilyName>Farzindar</FamilyName></EditorName><Contact><Email>farzindar@nlptechnologies.ca</Email></Contact></Editor><Editor AffiliationIDS="Aff20"><EditorName DisplayOrder="Western"><GivenName>Vlado</GivenName><FamilyName>Kešelj</FamilyName></EditorName><Contact><Email>vlado@cs.dal.ca</Email></Contact></Editor><Affiliation ID="Aff19"><OrgName>NLP Technologies Inc.</OrgName><OrgAddress><Street>1255 University Street</Street><Postcode>H3B 3W9</Postcode><City>Montreal</City><State>Quebec</State><Country>Canada</Country></OrgAddress></Affiliation><Affiliation ID="Aff20"><OrgDivision>Dalhousie University</OrgDivision><OrgName>Faculty of Computer Science</OrgName><OrgAddress><Street>6050 University Ave, Halifax</Street><Postcode>B3H 1W5</Postcode><City>Nova Scotia</City><Country>Canada</Country></OrgAddress></Affiliation></EditorGroup></BookHeader><Part ID="Part2"><PartInfo TocLevels="0"><PartID>2</PartID><PartSequenceNumber>2</PartSequenceNumber><PartTitle>Regular Papers</PartTitle><PartChapterCount>22</PartChapterCount><PartContext><SeriesID>558</SeriesID><BookTitle>Advances in Artificial Intelligence</BookTitle></PartContext></PartInfo><SubPart ID="SubPart5"><SubPartInfo><SubPartID>5</SubPartID><SubPartSequenceNumber>5</SubPartSequenceNumber><SubPartTitle>Neural Networks and Swarm Optimization</SubPartTitle><SubPartChapterCount>3</SubPartChapterCount></SubPartInfo><Chapter ID="Chap18" Language="En"><ChapterInfo ChapterType="OriginalPaper" ContainsESM="No" NumberingDepth="2" NumberingStyle="ContentOnly" TocLevels="0"><ChapterID>18</ChapterID><ChapterDOI>10.1007/978-3-642-13059-5_18</ChapterDOI><ChapterSequenceNumber>18</ChapterSequenceNumber><ChapterTitle Language="En">Offline Recognition of Handwritten Numeral Characters with Polynomial Neural Networks Using Topological Features</ChapterTitle><ChapterFirstPage>173</ChapterFirstPage><ChapterLastPage>183</ChapterLastPage><ChapterCopyright><CopyrightHolderName>Springer-Verlag Berlin Heidelberg</CopyrightHolderName><CopyrightYear>2010</CopyrightYear></ChapterCopyright><ChapterGrants Type="Regular"><MetadataGrant Grant="OpenAccess"></MetadataGrant><AbstractGrant Grant="OpenAccess"></AbstractGrant><BodyPDFGrant Grant="Restricted"></BodyPDFGrant><BodyHTMLGrant Grant="Restricted"></BodyHTMLGrant><BibliographyGrant Grant="Restricted"></BibliographyGrant><ESMGrant Grant="Restricted"></ESMGrant></ChapterGrants><ChapterContext><SeriesID>558</SeriesID><PartID>2</PartID><BookID>978-3-642-13059-5</BookID><BookTitle>Advances in Artificial Intelligence</BookTitle></ChapterContext></ChapterInfo><ChapterHeader><AuthorGroup><Author AffiliationIDS="Aff21"><AuthorName DisplayOrder="Western"><GivenName>El-Sayed</GivenName><GivenName>M.</GivenName><FamilyName>El-Alfy</FamilyName></AuthorName><Contact><Email>alfy@kfupm.edu.sa</Email></Contact></Author><Affiliation ID="Aff21"><OrgDivision>College of Computer Sciences and Engineering</OrgDivision><OrgName>King Fahd University of Petroleum and Minerals</OrgName><OrgAddress><City>Dhahran</City><Postcode>31261</Postcode><Country>Saudi Arabia</Country></OrgAddress></Affiliation></AuthorGroup><Abstract ID="Abs1" Language="En"><Heading>Abstract</Heading><Para>Group-Method of Data Handling (GMDH) has been recognized as a powerful tool in machine learning. It has the potential to build predictive neural network models of polynomial functions using only a reduced set of features which minimizes the prediction error. This paper explores the offline recognition of isolated handwritten numeral characters described with non-Gaussian topological features using GMDH-based polynomial networks. In order to study the effectiveness of the proposed approach, we apply it on a publicly available dataset of isolated handwritten numerals and compare the results with five other state-of-the-art classifiers: multilayer Perceptron, support-vector machine, radial-basis function, naïve Bayes and rule-based classifiers. In addition to improving the classification accuracy and the per-class performance measures, using GMDH-based polynomial neural networks has led to significant feature dimensionality reduction.</Para></Abstract><KeywordGroup Language="En"><Heading>Keywords</Heading><Keyword>Machine learning</Keyword><Keyword>GMDH</Keyword><Keyword>Polynomial networks</Keyword><Keyword>Pattern recognition</Keyword><Keyword>Handwritten numeral character recognition</Keyword><Keyword>Non-Gaussian topological features</Keyword></KeywordGroup></ChapterHeader><NoBody></NoBody></Chapter></SubPart></Part></Book></Series></Publisher></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>Offline Recognition of Handwritten Numeral Characters with Polynomial Neural Networks Using Topological Features</title></titleInfo><titleInfo type="alternative" contentType="CDATA" lang="en"><title>Offline Recognition of Handwritten Numeral Characters with Polynomial Neural Networks Using Topological Features</title></titleInfo><name type="personal"><namePart type="given">El-Sayed</namePart><namePart type="given">M.</namePart><namePart type="family">El-Alfy</namePart><affiliation>College of Computer Sciences and Engineering, King Fahd University of Petroleum and Minerals, 31261, Dhahran, Saudi Arabia</affiliation><affiliation>E-mail: alfy@kfupm.edu.sa</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><originInfo><publisher>Springer Berlin Heidelberg</publisher><place><placeTerm type="text">Berlin, Heidelberg</placeTerm></place><dateIssued encoding="w3cdtf">2010</dateIssued><copyrightDate encoding="w3cdtf">2010</copyrightDate></originInfo><language><languageTerm type="code" authority="rfc3066">en</languageTerm><languageTerm type="code" authority="iso639-2b">eng</languageTerm></language><physicalDescription><internetMediaType>text/html</internetMediaType></physicalDescription><abstract lang="en">Abstract: Group-Method of Data Handling (GMDH) has been recognized as a powerful tool in machine learning. It has the potential to build predictive neural network models of polynomial functions using only a reduced set of features which minimizes the prediction error. This paper explores the offline recognition of isolated handwritten numeral characters described with non-Gaussian topological features using GMDH-based polynomial networks. In order to study the effectiveness of the proposed approach, we apply it on a publicly available dataset of isolated handwritten numerals and compare the results with five other state-of-the-art classifiers: multilayer Perceptron, support-vector machine, radial-basis function, naïve Bayes and rule-based classifiers. In addition to improving the classification accuracy and the per-class performance measures, using GMDH-based polynomial neural networks has led to significant feature dimensionality reduction.</abstract><relatedItem type="host"><titleInfo><title>Advances in Artificial Intelligence</title><subTitle>23rd Canadian Conference on Artificial Intelligence, Canadian AI 2010, Ottawa, Canada, May 31 – June 2, 2010. Proceedings</subTitle></titleInfo><name type="personal"><namePart type="given">Atefeh</namePart><namePart type="family">Farzindar</namePart><affiliation>NLP Technologies Inc., 1255 University Street, H3B 3W9, Montreal, Quebec, Canada</affiliation><affiliation>E-mail: farzindar@nlptechnologies.ca</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Vlado</namePart><namePart type="family">Kešelj</namePart><affiliation>Dalhousie University, Faculty of Computer Science, 6050 University Ave, Halifax, B3H 1W5, Nova Scotia, Canada</affiliation><affiliation>E-mail: vlado@cs.dal.ca</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><genre type="Book Series" displayLabel="Proceedings"></genre><originInfo><copyrightDate encoding="w3cdtf">2010</copyrightDate><issuance>monographic</issuance></originInfo><subject><genre>Book Subject Collection</genre><topic authority="SpringerSubjectCodes" authorityURI="SUCO11645">Computer Science</topic></subject><subject><genre>Book Subject Group</genre><topic authority="SpringerSubjectCodes" authorityURI="I">Computer Science</topic><topic authority="SpringerSubjectCodes" authorityURI="I21017">Artificial Intelligence (incl. Robotics)</topic><topic authority="SpringerSubjectCodes" authorityURI="I2203X">Pattern Recognition</topic><topic authority="SpringerSubjectCodes" authorityURI="I21041">Language Translation and Linguistics</topic><topic authority="SpringerSubjectCodes" authorityURI="I18040">Information Systems Applications (incl.Internet)</topic><topic authority="SpringerSubjectCodes" authorityURI="I18032">Information Storage and Retrieval</topic><topic authority="SpringerSubjectCodes" authorityURI="I18030">Data Mining and Knowledge Discovery</topic></subject><identifier type="DOI">10.1007/978-3-642-13059-5</identifier><identifier type="ISBN">978-3-642-13058-8</identifier><identifier type="eISBN">978-3-642-13059-5</identifier><identifier type="ISSN">0302-9743</identifier><identifier type="eISSN">1611-3349</identifier><identifier type="BookTitleID">211296</identifier><identifier type="BookID">978-3-642-13059-5</identifier><identifier type="BookChapterCount">63</identifier><identifier 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type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Josef</namePart><namePart type="family">Kittler</namePart><affiliation>University of Surrey, Guildford, UK</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Jon</namePart><namePart type="given">M.</namePart><namePart type="family">Kleinberg</namePart><affiliation>Cornell University, Ithaca, NY, USA</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Friedemann</namePart><namePart type="family">Mattern</namePart><affiliation>ETH Zurich, Zurich, Switzerland</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">John</namePart><namePart type="given">C.</namePart><namePart type="family">Mitchell</namePart><affiliation>Stanford University, Stanford, CA, USA</affiliation><role><roleTerm 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