Modeling Context as Statistical Dependence
Identifieur interne : 000240 ( Istex/Corpus ); précédent : 000239; suivant : 000241Modeling Context as Statistical Dependence
Auteurs : Sriharsha Veeramachaneni ; Prateek Sarkar ; George NagySource :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2005.
Abstract
Abstract: Theories of context in logic enable reasoning and deduction in contexts represented as formal objects. Such theories are not readily applicable to systems that learn by induction from a set of examples. Probabilistic graphical models already provide the tools to exploit context represented as statistical dependences, thereby providing a unified methodology to incorporate context information in learning and inference. Drawing on a case study from optical character recognition, we present the various types of dependences that can occur in pattern classification problems and how such dependences can be exploited to increase classification accuracy. Learning under different conditions require differing amounts and kinds of samples and different trade-offs between modeling error due to overly strict independence assumptions and estimation error of models that are too elaborate for the size of the available training set. With a series of examples based on frames of two patterns we show how each kind of dependence can be represented using graphical models and present examples from other disciplines where the particular dependence frequently occurs.
Url:
DOI: 10.1007/11508373_39
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Such theories are not readily applicable to systems that learn by induction from a set of examples. Probabilistic graphical models already provide the tools to exploit context represented as statistical dependences, thereby providing a unified methodology to incorporate context information in learning and inference. Drawing on a case study from optical character recognition, we present the various types of dependences that can occur in pattern classification problems and how such dependences can be exploited to increase classification accuracy. Learning under different conditions require differing amounts and kinds of samples and different trade-offs between modeling error due to overly strict independence assumptions and estimation error of models that are too elaborate for the size of the available training set. 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type="seriesId">1244</biblScope></series><idno type="istex">50993A728920CA2485F7D3737DD68103573C8579</idno><idno type="DOI">10.1007/11508373_39</idno><idno type="ChapterID">39</idno><idno type="ChapterID">Chap39</idno></biblStruct></sourceDesc></fileDesc><profileDesc><creation><date>2005</date></creation><langUsage><language ident="en">en</language></langUsage><abstract xml:lang="en"><p>Abstract: Theories of context in logic enable reasoning and deduction in contexts represented as formal objects. Such theories are not readily applicable to systems that learn by induction from a set of examples. Probabilistic graphical models already provide the tools to exploit context represented as statistical dependences, thereby providing a unified methodology to incorporate context information in learning and inference. Drawing on a case study from optical character recognition, we present the various types of dependences that can occur in pattern classification problems and how such dependences can be exploited to increase classification accuracy. Learning under different conditions require differing amounts and kinds of samples and different trade-offs between modeling error due to overly strict independence assumptions and estimation error of models that are too elaborate for the size of the available training set. With a series of examples based on frames of two patterns we show how each kind of dependence can be represented using graphical models and present examples from other disciplines where the particular dependence frequently occurs.</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>I16048</label><label>I23028</label><label>I23036</label><item><term>Computer Science</term></item><item><term>Artificial Intelligence (incl. 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Proceedings</BookSubTitle><BookVolumeNumber>3554</BookVolumeNumber><BookSequenceNumber>3554</BookSequenceNumber><BookDOI>10.1007/b137917</BookDOI><BookTitleID>125001</BookTitleID><BookPrintISBN>978-3-540-26924-3</BookPrintISBN><BookElectronicISBN>978-3-540-31890-3</BookElectronicISBN><BookChapterCount>42</BookChapterCount><BookCopyright><CopyrightHolderName>Springer-Verlag Berlin Heidelberg</CopyrightHolderName><CopyrightYear>2005</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="I16048" Priority="2" Type="Secondary">Mathematical Logic and Formal Languages</BookSubject><BookSubject Code="I23028" Priority="3" Type="Secondary">Computer Appl. in Social and Behavioral Sciences</BookSubject><BookSubject Code="I23036" Priority="4" Type="Secondary">Computer Appl. in Arts and Humanities</BookSubject><SubjectCollection Code="SUCO11645">Computer Science</SubjectCollection></BookSubjectGroup><BookContext><SeriesID>558</SeriesID><SubSeriesID>1244</SubSeriesID></BookContext></BookInfo><BookHeader><EditorGroup><Editor AffiliationIDS="Aff18"><EditorName DisplayOrder="Western"><GivenName>Anind</GivenName><FamilyName>Dey</FamilyName></EditorName><Contact><Email>anind@cs.cmu.edu</Email></Contact></Editor><Editor AffiliationIDS="Aff19"><EditorName DisplayOrder="Western"><GivenName>Boicho</GivenName><FamilyName>Kokinov</FamilyName></EditorName><Contact><Email>bkokinov@nbu.bg</Email></Contact></Editor><Editor 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ID="Aff20"><OrgDivision>Computer Science Department</OrgDivision><OrgName>Indiana University</OrgName><OrgAddress><Postcode>47405</Postcode><City>Bloomington</City><State>IN</State><Country>U.S.A.</Country></OrgAddress></Affiliation><Affiliation ID="Aff21"><OrgDivision>Department of Computer Science</OrgDivision><OrgName>University of Maine</OrgName><OrgAddress><Postcode>04469</Postcode><City>Orono</City><State>Maine</State><Country>USA</Country></OrgAddress></Affiliation></EditorGroup></BookHeader><Chapter ID="Chap39" Language="En"><ChapterInfo ChapterType="OriginalPaper" ContainsESM="No" NumberingDepth="2" NumberingStyle="ContentOnly" TocLevels="0"><ChapterID>39</ChapterID><ChapterDOI>10.1007/11508373_39</ChapterDOI><ChapterSequenceNumber>39</ChapterSequenceNumber><ChapterTitle Language="En">Modeling Context as Statistical Dependence</ChapterTitle><ChapterFirstPage>515</ChapterFirstPage><ChapterLastPage>528</ChapterLastPage><ChapterCopyright><CopyrightHolderName>Springer-Verlag Berlin Heidelberg</CopyrightHolderName><CopyrightYear>2005</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><BookID>978-3-540-31890-3</BookID><BookTitle>Modeling and Using Context</BookTitle></ChapterContext></ChapterInfo><ChapterHeader><AuthorGroup><Author AffiliationIDS="Aff22"><AuthorName DisplayOrder="Western"><GivenName>Sriharsha</GivenName><FamilyName>Veeramachaneni</FamilyName></AuthorName><Contact><Email>sriharsha@itc.it</Email></Contact></Author><Author AffiliationIDS="Aff23"><AuthorName DisplayOrder="Western"><GivenName>Prateek</GivenName><FamilyName>Sarkar</FamilyName></AuthorName><Contact><Email>psarkar@parc.com</Email></Contact></Author><Author AffiliationIDS="Aff24"><AuthorName DisplayOrder="Western"><GivenName>George</GivenName><FamilyName>Nagy</FamilyName></AuthorName><Contact><Email>nagy@ecse.rpi.edu</Email></Contact></Author><Affiliation ID="Aff22"><OrgDivision>SRA Division</OrgDivision><OrgName>ITC-IRST</OrgName><OrgAddress><City>Povo</City><State>TN</State><Postcode>38057</Postcode><Country>Italy</Country></OrgAddress></Affiliation><Affiliation ID="Aff23"><OrgName>Palo Alto Research Centr</OrgName><OrgAddress><Street>3333 Coyote Hill Road</Street><City>Palo Alto</City><State>CA</State><Postcode>94304</Postcode><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff24"><OrgDivision>ECSE Dept.</OrgDivision><OrgName>Rensselaer Polytechnic Institute</OrgName><OrgAddress><Street>110 Eighth Street</Street><City>Troy</City><State>NY</State><Postcode>12180</Postcode><Country>USA</Country></OrgAddress></Affiliation></AuthorGroup><Abstract ID="Abs1" Language="En"><Heading>Abstract</Heading><Para>Theories of context in logic enable reasoning and deduction in contexts represented as formal objects. Such theories are not readily applicable to systems that learn by induction from a set of examples. Probabilistic graphical models already provide the tools to exploit context represented as statistical dependences, thereby providing a unified methodology to incorporate context information in learning and inference. Drawing on a case study from optical character recognition, we present the various types of dependences that can occur in pattern classification problems and how such dependences can be exploited to increase classification accuracy. Learning under different conditions require differing amounts and kinds of samples and different trade-offs between modeling error due to overly strict independence assumptions and estimation error of models that are too elaborate for the size of the available training set. With a series of examples based on frames of two patterns we show how each kind of dependence can be represented using graphical models and present examples from other disciplines where the particular dependence frequently occurs.</Para></Abstract></ChapterHeader><NoBody></NoBody></Chapter></Book></Series></Publisher></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>Modeling Context as Statistical Dependence</title></titleInfo><titleInfo type="alternative" contentType="CDATA" lang="en"><title>Modeling Context as Statistical Dependence</title></titleInfo><name type="personal"><namePart type="given">Sriharsha</namePart><namePart type="family">Veeramachaneni</namePart><affiliation>SRA Division, ITC-IRST, 38057, Povo, TN, Italy</affiliation><affiliation>E-mail: sriharsha@itc.it</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Prateek</namePart><namePart type="family">Sarkar</namePart><affiliation>Palo Alto Research Centr, 3333 Coyote Hill Road, 94304, Palo Alto, CA, USA</affiliation><affiliation>E-mail: psarkar@parc.com</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">George</namePart><namePart type="family">Nagy</namePart><affiliation>ECSE Dept., Rensselaer Polytechnic Institute, 110 Eighth Street, 12180, Troy, NY, USA</affiliation><affiliation>E-mail: nagy@ecse.rpi.edu</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="conference [eBooks]" displayLabel="OriginalPaper"></genre><originInfo><publisher>Springer Berlin Heidelberg</publisher><place><placeTerm type="text">Berlin, Heidelberg</placeTerm></place><dateIssued encoding="w3cdtf">2005</dateIssued><copyrightDate encoding="w3cdtf">2005</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: Theories of context in logic enable reasoning and deduction in contexts represented as formal objects. Such theories are not readily applicable to systems that learn by induction from a set of examples. Probabilistic graphical models already provide the tools to exploit context represented as statistical dependences, thereby providing a unified methodology to incorporate context information in learning and inference. Drawing on a case study from optical character recognition, we present the various types of dependences that can occur in pattern classification problems and how such dependences can be exploited to increase classification accuracy. Learning under different conditions require differing amounts and kinds of samples and different trade-offs between modeling error due to overly strict independence assumptions and estimation error of models that are too elaborate for the size of the available training set. With a series of examples based on frames of two patterns we show how each kind of dependence can be represented using graphical models and present examples from other disciplines where the particular dependence frequently occurs.</abstract><relatedItem type="host"><titleInfo><title>Modeling and Using Context</title><subTitle>5thInternational and Interdisciplinary Conference CONTEXT 2005, Paris, France, July 5-8, 2005. 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USA</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Moshe</namePart><namePart type="given">Y.</namePart><namePart type="family">Vardi</namePart><affiliation>Rice University, Houston, TX, USA</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Gerhard</namePart><namePart type="family">Weikum</namePart><affiliation>Max-Planck Institute of Computer Science, Saarbruecken, Germany</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Jaime</namePart><namePart type="given">G.</namePart><namePart type="family">Carbonell</namePart><affiliation>Carnegie Mellon University, Pittsburgh, PA, USA</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Jörg</namePart><namePart type="family">Siekmann</namePart><affiliation>University of Saarland, Saarbrücken, Germany</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Anind</namePart><namePart type="family">Dey</namePart><affiliation>Human-Computer Interaction Institute, Carnegie Mellon University, 5000 Forbes Ave, 15213-3891, Pittsburgh, PA, USA</affiliation><affiliation>E-mail: anind@cs.cmu.edu</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Boicho</namePart><namePart type="family">Kokinov</namePart><affiliation>Central and East European Center for Cognitive Science, New Bulgarian University, 21 Montevideo Street, 1618, Sofia, Bulgaria</affiliation><affiliation>E-mail: bkokinov@nbu.bg</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">David</namePart><namePart type="family">Leake</namePart><affiliation>Computer Science Department, Indiana University, 47405, Bloomington, IN, 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