Pattern Learning and Recognition on Statistical Manifolds: An Information-Geometric Review
Identifieur interne : 001B74 ( Istex/Corpus ); précédent : 001B73; suivant : 001B75Pattern Learning and Recognition on Statistical Manifolds: An Information-Geometric Review
Auteurs : Frank NielsenSource :
- Lecture Notes in Computer Science [ 0302-9743 ]
Abstract
Abstract: We review the information-geometric framework for statistical pattern recognition: First, we explain the role of statistical similarity measures and distances in fundamental statistical pattern recognition problems. We then concisely review the main statistical distances and report a novel versatile family of divergences. Depending on their intrinsic complexity, the statistical patterns are learned by either atomic parametric distributions, semi-parametric finite mixtures, or non-parametric kernel density distributions. Those statistical patterns are interpreted and handled geometrically in statistical manifolds either as single points, weighted sparse point sets or non-weighted dense point sets. We explain the construction of the two prominent families of statistical manifolds: The Rao Riemannian manifolds with geodesic metric distances, and the Amari-Chentsov manifolds with dual asymmetric non-metric divergences. For the latter manifolds, when considering atomic distributions from the same exponential families (including the ubiquitous Gaussian and multinomial families), we end up with dually flat exponential family manifolds that play a crucial role in many applications. We compare the advantages and disadvantages of these two approaches from the algorithmic point of view. Finally, we conclude with further perspectives on how “geometric thinking” may spur novel pattern modeling and processing paradigms.
Url:
DOI: 10.1007/978-3-642-39140-8_1
Links to Exploration step
ISTEX:77ECD0DEF000EEEF43CA1A6F4C4FEC696F8A1ABALe document en format XML
<record><TEI wicri:istexFullTextTei="biblStruct"><teiHeader><fileDesc><titleStmt><title xml:lang="en">Pattern Learning and Recognition on Statistical Manifolds: An Information-Geometric Review</title><author><name sortKey="Nielsen, Frank" sort="Nielsen, Frank" uniqKey="Nielsen F" first="Frank" last="Nielsen">Frank Nielsen</name><affiliation><mods:affiliation>Sony Computer Science Laboratories, Inc., Tokyo, Japan</mods:affiliation></affiliation><affiliation><mods:affiliation>E-mail: Frank.Nielsen@acm.org</mods:affiliation></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">ISTEX</idno><idno type="RBID">ISTEX:77ECD0DEF000EEEF43CA1A6F4C4FEC696F8A1ABA</idno><date when="2013" year="2013">2013</date><idno type="doi">10.1007/978-3-642-39140-8_1</idno><idno type="url">https://api.istex.fr/ark:/67375/HCB-51Z96TLC-5/fulltext.pdf</idno><idno type="wicri:Area/Istex/Corpus">001B74</idno><idno type="wicri:explorRef" wicri:stream="Istex" wicri:step="Corpus" wicri:corpus="ISTEX">001B74</idno></publicationStmt><sourceDesc><biblStruct><analytic><title level="a" type="main" xml:lang="en">Pattern Learning and Recognition on Statistical Manifolds: An Information-Geometric Review</title><author><name sortKey="Nielsen, Frank" sort="Nielsen, Frank" uniqKey="Nielsen F" first="Frank" last="Nielsen">Frank Nielsen</name><affiliation><mods:affiliation>Sony Computer Science Laboratories, Inc., Tokyo, Japan</mods:affiliation></affiliation><affiliation><mods:affiliation>E-mail: Frank.Nielsen@acm.org</mods:affiliation></affiliation></author></analytic><monogr></monogr><series><title level="s" type="main" xml:lang="en">Lecture Notes in Computer Science</title><idno type="ISSN">0302-9743</idno><idno type="eISSN">1611-3349</idno><idno type="ISSN">0302-9743</idno></series></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">0302-9743</idno></seriesStmt></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Abstract: We review the information-geometric framework for statistical pattern recognition: First, we explain the role of statistical similarity measures and distances in fundamental statistical pattern recognition problems. We then concisely review the main statistical distances and report a novel versatile family of divergences. Depending on their intrinsic complexity, the statistical patterns are learned by either atomic parametric distributions, semi-parametric finite mixtures, or non-parametric kernel density distributions. Those statistical patterns are interpreted and handled geometrically in statistical manifolds either as single points, weighted sparse point sets or non-weighted dense point sets. We explain the construction of the two prominent families of statistical manifolds: The Rao Riemannian manifolds with geodesic metric distances, and the Amari-Chentsov manifolds with dual asymmetric non-metric divergences. For the latter manifolds, when considering atomic distributions from the same exponential families (including the ubiquitous Gaussian and multinomial families), we end up with dually flat exponential family manifolds that play a crucial role in many applications. We compare the advantages and disadvantages of these two approaches from the algorithmic point of view. Finally, we conclude with further perspectives on how “geometric thinking” may spur novel pattern modeling and processing paradigms.</div></front></TEI><istex><corpusName>springer-ebooks</corpusName><author><json:item><name>Frank Nielsen</name><affiliations><json:string>Sony Computer Science Laboratories, Inc., Tokyo, Japan</json:string><json:string>E-mail: Frank.Nielsen@acm.org</json:string></affiliations></json:item></author><subject><json:item><lang><json:string>eng</json:string></lang><value>Statistical manifolds</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>mixture modeling</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>kernel density estimator</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>exponential families</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>clustering</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>Voronoi diagrams</value></json:item></subject><arkIstex>ark:/67375/HCB-51Z96TLC-5</arkIstex><language><json:string>eng</json:string></language><originalGenre><json:string>OriginalPaper</json:string></originalGenre><abstract>Abstract: We review the information-geometric framework for statistical pattern recognition: First, we explain the role of statistical similarity measures and distances in fundamental statistical pattern recognition problems. We then concisely review the main statistical distances and report a novel versatile family of divergences. Depending on their intrinsic complexity, the statistical patterns are learned by either atomic parametric distributions, semi-parametric finite mixtures, or non-parametric kernel density distributions. Those statistical patterns are interpreted and handled geometrically in statistical manifolds either as single points, weighted sparse point sets or non-weighted dense point sets. We explain the construction of the two prominent families of statistical manifolds: The Rao Riemannian manifolds with geodesic metric distances, and the Amari-Chentsov manifolds with dual asymmetric non-metric divergences. For the latter manifolds, when considering atomic distributions from the same exponential families (including the ubiquitous Gaussian and multinomial families), we end up with dually flat exponential family manifolds that play a crucial role in many applications. We compare the advantages and disadvantages of these two approaches from the algorithmic point of view. Finally, we conclude with further perspectives on how “geometric thinking” may spur novel pattern modeling and processing paradigms.</abstract><qualityIndicators><score>9.28</score><pdfWordCount>8720</pdfWordCount><pdfCharCount>50320</pdfCharCount><pdfVersion>1.6</pdfVersion><pdfPageCount>25</pdfPageCount><pdfPageSize>439.363 x 666.131 pts</pdfPageSize><refBibsNative>false</refBibsNative><abstractWordCount>190</abstractWordCount><abstractCharCount>1439</abstractCharCount><keywordCount>6</keywordCount></qualityIndicators><title>Pattern Learning and Recognition on Statistical Manifolds: An Information-Geometric Review</title><chapterId><json:string>1</json:string><json:string>Chap1</json:string></chapterId><genre><json:string>conference</json:string></genre><serie><title>Lecture Notes in Computer Science</title><language><json:string>unknown</json:string></language><copyrightDate>2013</copyrightDate><issn><json:string>0302-9743</json:string></issn><eissn><json:string>1611-3349</json:string></eissn><editor><json:item><name>David Hutchison</name><affiliations><json:string>Lancaster University, Lancaster, UK</json:string></affiliations></json:item><json:item><name>Takeo Kanade</name><affiliations><json:string>Carnegie Mellon University, Pittsburgh, PA, USA</json:string></affiliations></json:item><json:item><name>Josef Kittler</name><affiliations><json:string>University of Surrey, Guildford, UK</json:string></affiliations></json:item><json:item><name>Jon M. Kleinberg</name><affiliations><json:string>Cornell University, Ithaca, NY, USA</json:string></affiliations></json:item><json:item><name>Friedemann Mattern</name><affiliations><json:string>ETH Zurich, Zurich, Switzerland</json:string></affiliations></json:item><json:item><name>John C. Mitchell</name><affiliations><json:string>Stanford University, Stanford, CA, USA</json:string></affiliations></json:item><json:item><name>Moni Naor</name><affiliations><json:string>Weizmann Institute of Science, Rehovot, Israel</json:string></affiliations></json:item><json:item><name>Oscar Nierstrasz</name><affiliations><json:string>University of Bern, Bern, Switzerland</json:string></affiliations></json:item><json:item><name>C. Pandu Rangan</name><affiliations><json:string>Indian Institute of Technology, Madras, India</json:string></affiliations></json:item><json:item><name>Bernhard Steffen</name><affiliations><json:string>University of Dortmund, Dortmund, Germany</json:string></affiliations></json:item><json:item><name>Madhu Sudan</name><affiliations><json:string>Massachusetts Institute of Technology, MA, USA</json:string></affiliations></json:item><json:item><name>Demetri Terzopoulos</name><affiliations><json:string>University of California, Los Angeles, CA, USA</json:string></affiliations></json:item><json:item><name>Doug Tygar</name><affiliations><json:string>University of California, Berkeley, CA, USA</json:string></affiliations></json:item><json:item><name>Moshe Y. Vardi</name><affiliations><json:string>Rice University, Houston, TX, USA</json:string></affiliations></json:item><json:item><name>Gerhard Weikum</name><affiliations><json:string>Max-Planck Institute of Computer Science, Saarbrücken, Germany</json:string></affiliations></json:item></editor></serie><host><title>Similarity-Based Pattern Recognition</title><language><json:string>unknown</json:string></language><copyrightDate>2013</copyrightDate><doi><json:string>10.1007/978-3-642-39140-8</json:string></doi><issn><json:string>0302-9743</json:string></issn><eissn><json:string>1611-3349</json:string></eissn><eisbn><json:string>978-3-642-39140-8</json:string></eisbn><bookId><json:string>978-3-642-39140-8</json:string></bookId><isbn><json:string>978-3-642-39139-2</json:string></isbn><volume>7953</volume><pages><first>1</first><last>25</last></pages><genre><json:string>book-series</json:string></genre><editor><json:item><name>Edwin Hancock</name><affiliations><json:string>Department of Computer Science, University of York, Deramore Lane, YO10 5GH, York, UK</json:string><json:string>E-mail: erh@cs.york.ac.uk</json:string></affiliations></json:item><json:item><name>Marcello Pelillo</name><affiliations><json:string>DAIS, Università Ca’ Foscari, Via Torino 155, 30172, Venice, Italy</json:string><json:string>E-mail: pelillo@dsi.unive.it</json:string></affiliations></json:item></editor><subject><json:item><value>Computer Science</value></json:item><json:item><value>Computer Science</value></json:item><json:item><value>Pattern Recognition</value></json:item><json:item><value>Image Processing and Computer Vision</value></json:item><json:item><value>Artificial Intelligence (incl. Robotics)</value></json:item><json:item><value>Database Management</value></json:item><json:item><value>Algorithm Analysis and Problem Complexity</value></json:item><json:item><value>Information Systems Applications (incl. Internet)</value></json:item></subject></host><ark><json:string>ark:/67375/HCB-51Z96TLC-5</json:string></ark><publicationDate>2013</publicationDate><copyrightDate>2013</copyrightDate><doi><json:string>10.1007/978-3-642-39140-8_1</json:string></doi><id>77ECD0DEF000EEEF43CA1A6F4C4FEC696F8A1ABA</id><score>1</score><fulltext><json:item><extension>pdf</extension><original>true</original><mimetype>application/pdf</mimetype><uri>https://api.istex.fr/ark:/67375/HCB-51Z96TLC-5/fulltext.pdf</uri></json:item><json:item><extension>zip</extension><original>false</original><mimetype>application/zip</mimetype><uri>https://api.istex.fr/ark:/67375/HCB-51Z96TLC-5/bundle.zip</uri></json:item><istex:fulltextTEI uri="https://api.istex.fr/ark:/67375/HCB-51Z96TLC-5/fulltext.tei"><teiHeader><fileDesc><titleStmt><title level="a" type="main" xml:lang="en">Pattern Learning and Recognition on Statistical Manifolds: An Information-Geometric Review</title></titleStmt><publicationStmt><authority>ISTEX</authority><availability><licence>Springer-Verlag Berlin Heidelberg</licence></availability><date when="2013">2013</date></publicationStmt><notesStmt><note type="conference" source="proceedings" scheme="https://content-type.data.istex.fr/ark:/67375/XTP-BFHXPBJJ-3">conference</note><note type="publication-type" subtype="book-series" scheme="https://publication-type.data.istex.fr/ark:/67375/JMC-0G6R5W5T-Z">book-series</note></notesStmt><sourceDesc><biblStruct><analytic><title level="a" type="main" xml:lang="en">Pattern Learning and Recognition on Statistical Manifolds: An Information-Geometric Review</title><author><persName><forename type="first">Frank</forename><surname>Nielsen</surname></persName><email>Frank.Nielsen@acm.org</email><affiliation><idno type="GRID" subtype="Institution">grid.452725.3</idno><idno type="ISNI" subtype="Institution">0000000417640071</idno><orgName type="institution">Sony Computer Science Laboratories, Inc.</orgName><address><settlement>Tokyo</settlement><country key="JP">JAPAN</country></address></affiliation></author><idno type="istex">77ECD0DEF000EEEF43CA1A6F4C4FEC696F8A1ABA</idno><idno type="ark">ark:/67375/HCB-51Z96TLC-5</idno><idno type="DOI">10.1007/978-3-642-39140-8_1</idno></analytic><monogr><title level="m" type="main">Similarity-Based Pattern Recognition</title><title level="m" type="sub">Second International Workshop, SIMBAD 2013, York, UK, July 3-5, 2013. Proceedings</title><idno type="DOI">10.1007/978-3-642-39140-8</idno><idno type="book-id">978-3-642-39140-8</idno><idno type="ISBN">978-3-642-39139-2</idno><idno type="eISBN">978-3-642-39140-8</idno><idno type="chapter-id">Chap1</idno><editor><persName><forename type="first">Edwin</forename><surname>Hancock</surname></persName><email>erh@cs.york.ac.uk</email><affiliation><idno type="GRID" subtype="Institution">grid.5685.e</idno><idno type="ISNI" subtype="Institution">0000000419369668</idno><orgName type="department">Department of Computer Science</orgName><orgName type="institution">University of York</orgName><address><street>Deramore Lane</street><postCode>YO10 5GH</postCode><settlement>York</settlement><country key="GB">UNITED KINGDOM</country></address></affiliation></editor><editor><persName><forename type="first">Marcello</forename><surname>Pelillo</surname></persName><email>pelillo@dsi.unive.it</email><affiliation><idno type="GRID" subtype="Institution">grid.7240.1</idno><idno type="ISNI" subtype="Institution">0000000417630578</idno><orgName type="department">DAIS</orgName><orgName type="institution">Università Ca’ Foscari</orgName><address><street>Via Torino 155</street><postCode>30172</postCode><settlement>Venice</settlement><country key="IT">ITALY</country></address></affiliation></editor><meeting><title type="name">International Workshop on Similarity-Based Pattern Recognition</title><title type="abbr">SIMBAD</title><idno type="conf-number">2</idno><idno type="Springer">simbad</idno><idno type="DBLP">simbad</idno><idno type="conf-ID">simbad2013</idno><settlement>York</settlement><country>UK</country><date from="20130703" to="20130705"></date></meeting><imprint><biblScope unit="vol">7953</biblScope><biblScope unit="page" from="1">1</biblScope><biblScope unit="page" to="25">25</biblScope><biblScope unit="chapter-count">19</biblScope></imprint></monogr><series><title level="s" type="main" xml:lang="en">Lecture Notes in Computer Science</title><editor><persName><forename type="first">David</forename><surname>Hutchison</surname></persName><affiliation><idno type="GRID" subtype="Institution">grid.9835.7</idno><idno type="ISNI" subtype="Institution">0000000081906402</idno><orgName type="institution">Lancaster University</orgName><address><settlement>Lancaster</settlement><country key="GB">UNITED KINGDOM</country></address></affiliation></editor><editor><persName><forename type="first">Takeo</forename><surname>Kanade</surname></persName><affiliation><idno type="GRID" subtype="Institution">grid.147455.6</idno><idno type="ISNI" subtype="Institution">0000000120970344</idno><orgName type="institution">Carnegie Mellon University</orgName><address><settlement>Pittsburgh</settlement><region>PA</region><country key="US">UNITED STATES</country></address></affiliation></editor><editor><persName><forename type="first">Josef</forename><surname>Kittler</surname></persName><affiliation><idno type="GRID" subtype="Institution">grid.5475.3</idno><idno type="ISNI" subtype="Institution">0000000404074824</idno><orgName type="institution">University of Surrey</orgName><address><settlement>Guildford</settlement><country key="GB">UNITED KINGDOM</country></address></affiliation></editor><editor><persName><forename type="first">Jon</forename><forename type="first">M.</forename><surname>Kleinberg</surname></persName><affiliation><idno type="GRID" subtype="Institution">grid.5386.8</idno><idno type="ISNI" subtype="Institution">000000041936877X</idno><orgName type="institution">Cornell University</orgName><address><settlement>Ithaca</settlement><region>NY</region><country key="US">UNITED STATES</country></address></affiliation></editor><editor><persName><forename type="first">Friedemann</forename><surname>Mattern</surname></persName><affiliation><idno type="GRID" subtype="Institution">grid.5801.c</idno><idno type="ISNI" subtype="Institution">0000000121562780</idno><orgName type="institution">ETH Zurich</orgName><address><settlement>Zurich</settlement><country key="CH">SWITZERLAND</country></address></affiliation></editor><editor><persName><forename type="first">John</forename><forename type="first">C.</forename><surname>Mitchell</surname></persName><affiliation><idno type="GRID" subtype="Institution">grid.168010.e</idno><idno type="ISNI" subtype="Institution">0000000419368956</idno><orgName type="institution">Stanford University</orgName><address><settlement>Stanford</settlement><region>CA</region><country key="US">UNITED STATES</country></address></affiliation></editor><editor><persName><forename type="first">Moni</forename><surname>Naor</surname></persName><affiliation><idno type="GRID" subtype="Institution">grid.13992.30</idno><idno type="ISNI" subtype="Institution">0000000406047563</idno><orgName type="institution">Weizmann Institute of Science</orgName><address><settlement>Rehovot</settlement><country key="IL">ISRAEL</country></address></affiliation></editor><editor><persName><forename type="first">Oscar</forename><surname>Nierstrasz</surname></persName><affiliation><idno type="GRID" subtype="Institution">grid.5734.5</idno><idno type="ISNI" subtype="Institution">0000000107265157</idno><orgName type="institution">University of Bern</orgName><address><settlement>Bern</settlement><country key="CH">SWITZERLAND</country></address></affiliation></editor><editor><persName><forename type="first">C.</forename><surname>Pandu Rangan</surname></persName><affiliation><idno type="GRID" subtype="Institution">grid.417969.4</idno><idno type="ISNI" subtype="Institution">0000000123151926</idno><orgName type="institution">Indian Institute of Technology</orgName><address><settlement>Madras</settlement><country key="IN">INDIA</country></address></affiliation></editor><editor><persName><forename type="first">Bernhard</forename><surname>Steffen</surname></persName><affiliation><idno type="GRID" subtype="Institution">grid.5675.1</idno><idno type="ISNI" subtype="Institution">0000000104169637</idno><orgName type="institution">University of Dortmund</orgName><address><settlement>Dortmund</settlement><country key="DE">GERMANY</country></address></affiliation></editor><editor><persName><forename type="first">Madhu</forename><surname>Sudan</surname></persName><affiliation><idno type="GRID" subtype="Institution">grid.116068.8</idno><idno type="ISNI" subtype="Institution">0000000123412786</idno><orgName type="institution">Massachusetts Institute of Technology</orgName><address><region>MA</region><country key="US">UNITED STATES</country></address></affiliation></editor><editor><persName><forename type="first">Demetri</forename><surname>Terzopoulos</surname></persName><affiliation><idno type="GRID" subtype="Institution">grid.19006.3e</idno><idno type="ISNI" subtype="Institution">0000000096326718</idno><orgName type="institution">University of California</orgName><address><settlement>Los Angeles</settlement><region>CA</region><country key="US">UNITED STATES</country></address></affiliation></editor><editor><persName><forename type="first">Doug</forename><surname>Tygar</surname></persName><affiliation><idno type="GRID" subtype="Institution">grid.47840.3f</idno><idno type="ISNI" subtype="Institution">0000000121817878</idno><orgName type="institution">University of California</orgName><address><settlement>Berkeley</settlement><region>CA</region><country key="US">UNITED STATES</country></address></affiliation></editor><editor><persName><forename type="first">Moshe</forename><forename type="first">Y.</forename><surname>Vardi</surname></persName><affiliation><idno type="GRID" subtype="Institution">grid.21940.3e</idno><idno type="ISNI" subtype="Institution"> 0000000419368278</idno><orgName type="institution">Rice University</orgName><address><settlement>Houston</settlement><region>TX</region><country key="US">UNITED STATES</country></address></affiliation></editor><editor><persName><forename type="first">Gerhard</forename><surname>Weikum</surname></persName><affiliation><idno type="GRID" subtype="Institution">grid.419607.d</idno><idno type="ISNI" subtype="Institution">0000000120969941</idno><orgName type="institution">Max-Planck Institute of Computer Science</orgName><address><settlement>Saarbrücken</settlement><country key="DE">GERMANY</country></address></affiliation></editor><idno type="pISSN">0302-9743</idno><idno type="eISSN">1611-3349</idno><idno type="seriesID">558</idno></series></biblStruct></sourceDesc></fileDesc><profileDesc><abstract xml:lang="en"><head>Abstract</head><p>We review the <hi rend="italic">information-geometric</hi> framework for statistical pattern recognition: First, we explain the role of statistical similarity measures and distances in fundamental statistical pattern recognition problems. We then concisely review the main statistical distances and report a novel versatile family of divergences. Depending on their intrinsic complexity, the statistical patterns are learned by either atomic parametric distributions, semi-parametric finite mixtures, or non-parametric kernel density distributions. Those statistical patterns are interpreted and handled geometrically in <hi rend="italic">statistical manifolds</hi> either as single points, weighted sparse point sets or non-weighted dense point sets. We explain the construction of the two prominent families of statistical manifolds: The Rao Riemannian manifolds with geodesic metric distances, and the Amari-Chentsov manifolds with dual asymmetric non-metric divergences. For the latter manifolds, when considering atomic distributions from the same exponential families (including the ubiquitous Gaussian and multinomial families), we end up with dually flat exponential family manifolds that play a crucial role in many applications. We compare the advantages and disadvantages of these two approaches from the algorithmic point of view. Finally, we conclude with further perspectives on how “geometric thinking” may spur novel pattern modeling and processing paradigms.</p></abstract><textClass ana="keyword"><keywords xml:lang="en"><term>Statistical manifolds</term><term>mixture modeling</term><term>kernel density estimator</term><term>exponential families</term><term>clustering</term><term>Voronoi diagrams</term></keywords></textClass><textClass ana="subject"><keywords scheme="book-subject-collection"><list><label>SUCO11645</label><item><term>Computer Science</term></item></list></keywords></textClass><textClass ana="subject"><keywords scheme="book-subject"><list><label>SCI</label><item><term type="Primary">Computer Science</term></item><label>SCI2203X</label><item><term type="Secondary" subtype="priority-1">Pattern Recognition</term></item><label>SCI22021</label><item><term type="Secondary" subtype="priority-2">Image Processing and Computer Vision</term></item><label>SCI21017</label><item><term type="Secondary" subtype="priority-3">Artificial Intelligence (incl. Robotics)</term></item><label>SCI18024</label><item><term type="Secondary" subtype="priority-4">Database Management</term></item><label>SCI16021</label><item><term type="Secondary" subtype="priority-5">Algorithm Analysis and Problem Complexity</term></item><label>SCI18040</label><item><term type="Secondary" subtype="priority-6">Information Systems Applications (incl. Internet)</term></item></list></keywords></textClass><langUsage><language ident="EN"></language></langUsage></profileDesc></teiHeader></istex:fulltextTEI><json:item><extension>txt</extension><original>false</original><mimetype>text/plain</mimetype><uri>https://api.istex.fr/ark:/67375/HCB-51Z96TLC-5/fulltext.txt</uri></json:item></fulltext><metadata><istex:metadataXml wicri:clean="corpus springer-ebooks not found" wicri:toSee="no header"><istex:xmlDeclaration>version="1.0" encoding="UTF-8"</istex:xmlDeclaration><istex:docType PUBLIC="-//Springer-Verlag//DTD A++ V2.4//EN" URI="http://devel.springer.de/A++/V2.4/DTD/A++V2.4.dtd" name="istex:docType"></istex:docType><istex:document><Publisher><PublisherInfo><PublisherName>Springer Berlin Heidelberg</PublisherName><PublisherLocation>Berlin, Heidelberg</PublisherLocation><PublisherImprintName>Springer</PublisherImprintName></PublisherInfo><Series><SeriesInfo ID="Series558" SeriesType="Series" TocLevels="0"><SeriesID>558</SeriesID><SeriesPrintISSN>0302-9743</SeriesPrintISSN><SeriesElectronicISSN>1611-3349</SeriesElectronicISSN><SeriesTitle Language="En">Lecture Notes in Computer Science</SeriesTitle></SeriesInfo><SeriesHeader><EditorGroup><Editor AffiliationIDS="Aff1"><EditorName DisplayOrder="Western"><GivenName>David</GivenName><FamilyName>Hutchison</FamilyName></EditorName></Editor><Editor AffiliationIDS="Aff2"><EditorName DisplayOrder="Western"><GivenName>Takeo</GivenName><FamilyName>Kanade</FamilyName></EditorName></Editor><Editor AffiliationIDS="Aff3"><EditorName DisplayOrder="Western"><GivenName>Josef</GivenName><FamilyName>Kittler</FamilyName></EditorName></Editor><Editor AffiliationIDS="Aff4"><EditorName DisplayOrder="Western"><GivenName>Jon</GivenName><GivenName>M.</GivenName><FamilyName>Kleinberg</FamilyName></EditorName></Editor><Editor AffiliationIDS="Aff5"><EditorName DisplayOrder="Western"><GivenName>Friedemann</GivenName><FamilyName>Mattern</FamilyName></EditorName></Editor><Editor AffiliationIDS="Aff6"><EditorName DisplayOrder="Western"><GivenName>John</GivenName><GivenName>C.</GivenName><FamilyName>Mitchell</FamilyName></EditorName></Editor><Editor AffiliationIDS="Aff7"><EditorName DisplayOrder="Western"><GivenName>Moni</GivenName><FamilyName>Naor</FamilyName></EditorName></Editor><Editor AffiliationIDS="Aff8"><EditorName DisplayOrder="Western"><GivenName>Oscar</GivenName><FamilyName>Nierstrasz</FamilyName></EditorName></Editor><Editor AffiliationIDS="Aff9"><EditorName DisplayOrder="Western"><GivenName>C.</GivenName><FamilyName>Pandu Rangan</FamilyName></EditorName></Editor><Editor AffiliationIDS="Aff10"><EditorName DisplayOrder="Western"><GivenName>Bernhard</GivenName><FamilyName>Steffen</FamilyName></EditorName></Editor><Editor AffiliationIDS="Aff11"><EditorName DisplayOrder="Western"><GivenName>Madhu</GivenName><FamilyName>Sudan</FamilyName></EditorName></Editor><Editor AffiliationIDS="Aff12"><EditorName DisplayOrder="Western"><GivenName>Demetri</GivenName><FamilyName>Terzopoulos</FamilyName></EditorName></Editor><Editor AffiliationIDS="Aff13"><EditorName DisplayOrder="Western"><GivenName>Doug</GivenName><FamilyName>Tygar</FamilyName></EditorName></Editor><Editor AffiliationIDS="Aff14"><EditorName DisplayOrder="Western"><GivenName>Moshe</GivenName><GivenName>Y.</GivenName><FamilyName>Vardi</FamilyName></EditorName></Editor><Editor AffiliationIDS="Aff15"><EditorName DisplayOrder="Western"><GivenName>Gerhard</GivenName><FamilyName>Weikum</FamilyName></EditorName></Editor><Affiliation ID="Aff1"><OrgID Level="Institution" Type="GRID">grid.9835.7</OrgID><OrgID Level="Institution" Type="ISNI">0000000081906402</OrgID><OrgName>Lancaster University</OrgName><OrgAddress><City>Lancaster</City><Country>UK</Country></OrgAddress></Affiliation><Affiliation ID="Aff2"><OrgID Level="Institution" Type="GRID">grid.147455.6</OrgID><OrgID Level="Institution" Type="ISNI">0000000120970344</OrgID><OrgName>Carnegie Mellon University</OrgName><OrgAddress><City>Pittsburgh</City><State>PA</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff3"><OrgID Level="Institution" Type="GRID">grid.5475.3</OrgID><OrgID Level="Institution" Type="ISNI">0000000404074824</OrgID><OrgName>University of Surrey</OrgName><OrgAddress><City>Guildford</City><Country>UK</Country></OrgAddress></Affiliation><Affiliation ID="Aff4"><OrgID Level="Institution" Type="GRID">grid.5386.8</OrgID><OrgID Level="Institution" Type="ISNI">000000041936877X</OrgID><OrgName>Cornell University</OrgName><OrgAddress><City>Ithaca</City><State>NY</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff5"><OrgID Level="Institution" Type="GRID">grid.5801.c</OrgID><OrgID Level="Institution" Type="ISNI">0000000121562780</OrgID><OrgName>ETH Zurich</OrgName><OrgAddress><City>Zurich</City><Country>Switzerland</Country></OrgAddress></Affiliation><Affiliation ID="Aff6"><OrgID Level="Institution" Type="GRID">grid.168010.e</OrgID><OrgID Level="Institution" Type="ISNI">0000000419368956</OrgID><OrgName>Stanford University</OrgName><OrgAddress><City>Stanford</City><State>CA</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff7"><OrgID Level="Institution" Type="GRID">grid.13992.30</OrgID><OrgID Level="Institution" Type="ISNI">0000000406047563</OrgID><OrgName>Weizmann Institute of Science</OrgName><OrgAddress><City>Rehovot</City><Country>Israel</Country></OrgAddress></Affiliation><Affiliation ID="Aff8"><OrgID Level="Institution" Type="GRID">grid.5734.5</OrgID><OrgID Level="Institution" Type="ISNI">0000000107265157</OrgID><OrgName>University of Bern</OrgName><OrgAddress><City>Bern</City><Country>Switzerland</Country></OrgAddress></Affiliation><Affiliation ID="Aff9"><OrgID Level="Institution" Type="GRID">grid.417969.4</OrgID><OrgID Level="Institution" Type="ISNI">0000000123151926</OrgID><OrgName>Indian Institute of Technology</OrgName><OrgAddress><City>Madras</City><Country>India</Country></OrgAddress></Affiliation><Affiliation ID="Aff10"><OrgID Level="Institution" Type="GRID">grid.5675.1</OrgID><OrgID Level="Institution" Type="ISNI">0000000104169637</OrgID><OrgName>University of Dortmund</OrgName><OrgAddress><City>Dortmund</City><Country>Germany</Country></OrgAddress></Affiliation><Affiliation ID="Aff11"><OrgID Level="Institution" Type="GRID">grid.116068.8</OrgID><OrgID Level="Institution" Type="ISNI">0000000123412786</OrgID><OrgName>Massachusetts Institute of Technology</OrgName><OrgAddress><State>MA</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff12"><OrgID Level="Institution" Type="GRID">grid.19006.3e</OrgID><OrgID Level="Institution" Type="ISNI">0000000096326718</OrgID><OrgName>University of California</OrgName><OrgAddress><City>Los Angeles</City><State>CA</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff13"><OrgID Level="Institution" Type="GRID">grid.47840.3f</OrgID><OrgID Level="Institution" Type="ISNI">0000000121817878</OrgID><OrgName>University of California</OrgName><OrgAddress><City>Berkeley</City><State>CA</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff14"><OrgID Level="Institution" Type="GRID">grid.21940.3e</OrgID><OrgID Level="Institution" Type="ISNI"> 0000000419368278</OrgID><OrgName>Rice University</OrgName><OrgAddress><City>Houston</City><State>TX</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff15"><OrgID Level="Institution" Type="GRID">grid.419607.d</OrgID><OrgID Level="Institution" Type="ISNI">0000000120969941</OrgID><OrgName>Max-Planck Institute of Computer Science</OrgName><OrgAddress><City>Saarbrücken</City><Country>Germany</Country></OrgAddress></Affiliation></EditorGroup></SeriesHeader><Book Language="En"><BookInfo BookProductType="Proceedings" ContainsESM="No" Language="En" MediaType="eBook" NumberingDepth="2" NumberingStyle="ContentOnly" OutputMedium="All" TocLevels="0"><BookID>978-3-642-39140-8</BookID><BookTitle>Similarity-Based Pattern Recognition</BookTitle><BookSubTitle>Second International Workshop, SIMBAD 2013, York, UK, July 3-5, 2013. Proceedings</BookSubTitle><BookVolumeNumber>7953</BookVolumeNumber><BookSequenceNumber>7953</BookSequenceNumber><BookDOI>10.1007/978-3-642-39140-8</BookDOI><BookTitleID>316609</BookTitleID><BookPrintISBN>978-3-642-39139-2</BookPrintISBN><BookElectronicISBN>978-3-642-39140-8</BookElectronicISBN><BookChapterCount>19</BookChapterCount><BookCopyright><CopyrightHolderName>Springer-Verlag Berlin Heidelberg</CopyrightHolderName><CopyrightYear>2013</CopyrightYear></BookCopyright><BookSubjectGroup><BookSubject Code="SCI" Type="Primary">Computer Science</BookSubject><BookSubject Code="SCI2203X" Priority="1" Type="Secondary">Pattern Recognition</BookSubject><BookSubject Code="SCI22021" Priority="2" Type="Secondary">Image Processing and Computer Vision</BookSubject><BookSubject Code="SCI21017" Priority="3" Type="Secondary">Artificial Intelligence (incl. Robotics)</BookSubject><BookSubject Code="SCI18024" Priority="4" Type="Secondary">Database Management</BookSubject><BookSubject Code="SCI16021" Priority="5" Type="Secondary">Algorithm Analysis and Problem Complexity</BookSubject><BookSubject Code="SCI18040" Priority="6" Type="Secondary">Information Systems Applications (incl. Internet)</BookSubject><SubjectCollection Code="SUCO11645">Computer Science</SubjectCollection></BookSubjectGroup><BookContext><SeriesID>558</SeriesID></BookContext><ConferenceInfo><ConfSeriesName>International Workshop on Similarity-Based Pattern Recognition</ConfSeriesName><ConfSeriesID Type="Springer">simbad</ConfSeriesID><ConfSeriesID Type="DBLP">simbad</ConfSeriesID><ConfEventID Type="Springer">simbad2013</ConfEventID><ConfEventAbbreviation>SIMBAD</ConfEventAbbreviation><ConfNumber>2</ConfNumber><ConfEventLocation><City>York</City><Country>UK</Country></ConfEventLocation><ConfEventDateStart><Year>2013</Year><Month>7</Month><Day>3</Day></ConfEventDateStart><ConfEventDateEnd><Year>2013</Year><Month>7</Month><Day>5</Day></ConfEventDateEnd></ConferenceInfo></BookInfo><BookHeader><EditorGroup><Editor AffiliationIDS="Aff16"><EditorName DisplayOrder="Western"><GivenName>Edwin</GivenName><FamilyName>Hancock</FamilyName></EditorName><Contact><Email>erh@cs.york.ac.uk</Email></Contact></Editor><Editor AffiliationIDS="Aff17"><EditorName DisplayOrder="Western"><GivenName>Marcello</GivenName><FamilyName>Pelillo</FamilyName></EditorName><Contact><Email>pelillo@dsi.unive.it</Email></Contact></Editor><Affiliation ID="Aff16"><OrgID Level="Institution" Type="GRID">grid.5685.e</OrgID><OrgID Level="Institution" Type="ISNI">0000000419369668</OrgID><OrgDivision>Department of Computer Science</OrgDivision><OrgName>University of York</OrgName><OrgAddress><Street>Deramore Lane</Street><Postcode>YO10 5GH</Postcode><City>York</City><Country>UK</Country></OrgAddress></Affiliation><Affiliation ID="Aff17"><OrgID Level="Institution" Type="GRID">grid.7240.1</OrgID><OrgID Level="Institution" Type="ISNI">0000000417630578</OrgID><OrgDivision>DAIS</OrgDivision><OrgName>Università Ca’ Foscari</OrgName><OrgAddress><Street>Via Torino 155</Street><Postcode>30172</Postcode><City>Venice</City><Country>Italy</Country></OrgAddress></Affiliation></EditorGroup></BookHeader><Chapter ID="Chap1" Language="En"><ChapterInfo ChapterType="OriginalPaper" ContainsESM="No" NumberingDepth="2" NumberingStyle="ContentOnly" TocLevels="0"><ChapterID>1</ChapterID><ChapterDOI>10.1007/978-3-642-39140-8_1</ChapterDOI><ChapterSequenceNumber>1</ChapterSequenceNumber><ChapterTitle Language="En">Pattern Learning and Recognition on Statistical Manifolds: An Information-Geometric Review</ChapterTitle><ChapterFirstPage>1</ChapterFirstPage><ChapterLastPage>25</ChapterLastPage><ChapterCopyright><CopyrightHolderName>Springer-Verlag Berlin Heidelberg</CopyrightHolderName><CopyrightYear>2013</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>978-3-642-39140-8</BookID><BookTitle>Similarity-Based Pattern Recognition</BookTitle></ChapterContext></ChapterInfo><ChapterHeader><AuthorGroup><Author AffiliationIDS="Aff18"><AuthorName DisplayOrder="Western"><GivenName>Frank</GivenName><FamilyName>Nielsen</FamilyName></AuthorName><Contact><Email>Frank.Nielsen@acm.org</Email><URL>http://www.informationgeometry.org</URL></Contact></Author><Affiliation ID="Aff18"><OrgID Level="Institution" Type="GRID">grid.452725.3</OrgID><OrgID Level="Institution" Type="ISNI">0000000417640071</OrgID><OrgName>Sony Computer Science Laboratories, Inc.</OrgName><OrgAddress><City>Tokyo</City><Country>Japan</Country></OrgAddress></Affiliation></AuthorGroup><Abstract ID="Abs1" Language="En"><Heading>Abstract</Heading><Para>We review the <Emphasis Type="Italic">information-geometric</Emphasis> framework for statistical pattern recognition: First, we explain the role of statistical similarity measures and distances in fundamental statistical pattern recognition problems. We then concisely review the main statistical distances and report a novel versatile family of divergences. Depending on their intrinsic complexity, the statistical patterns are learned by either atomic parametric distributions, semi-parametric finite mixtures, or non-parametric kernel density distributions. Those statistical patterns are interpreted and handled geometrically in <Emphasis Type="Italic">statistical manifolds</Emphasis> either as single points, weighted sparse point sets or non-weighted dense point sets. We explain the construction of the two prominent families of statistical manifolds: The Rao Riemannian manifolds with geodesic metric distances, and the Amari-Chentsov manifolds with dual asymmetric non-metric divergences. For the latter manifolds, when considering atomic distributions from the same exponential families (including the ubiquitous Gaussian and multinomial families), we end up with dually flat exponential family manifolds that play a crucial role in many applications. We compare the advantages and disadvantages of these two approaches from the algorithmic point of view. Finally, we conclude with further perspectives on how “geometric thinking” may spur novel pattern modeling and processing paradigms.</Para></Abstract><KeywordGroup Language="En"><Heading>Keywords</Heading><Keyword>Statistical manifolds</Keyword><Keyword>mixture modeling</Keyword><Keyword>kernel density estimator</Keyword><Keyword>exponential families</Keyword><Keyword>clustering</Keyword><Keyword>Voronoi diagrams</Keyword></KeywordGroup></ChapterHeader><NoBody></NoBody></Chapter></Book></Series></Publisher><!-- Converted from LaTeX with LaTeX2A++ V3.1.38 --></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>Pattern Learning and Recognition on Statistical Manifolds: An Information-Geometric Review</title></titleInfo><titleInfo type="alternative" contentType="CDATA"><title>Pattern Learning and Recognition on Statistical Manifolds: An Information-Geometric Review</title></titleInfo><name type="personal"><namePart type="given">Frank</namePart><namePart type="family">Nielsen</namePart><affiliation>Sony Computer Science Laboratories, Inc., Tokyo, Japan</affiliation><affiliation>E-mail: Frank.Nielsen@acm.org</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre displayLabel="OriginalPaper" authority="ISTEX" authorityURI="https://content-type.data.istex.fr" type="conference" valueURI="https://content-type.data.istex.fr/ark:/67375/XTP-BFHXPBJJ-3">conference</genre><originInfo><publisher>Springer Berlin Heidelberg</publisher><place><placeTerm type="text">Berlin, Heidelberg</placeTerm></place><dateIssued encoding="w3cdtf">2013</dateIssued><copyrightDate encoding="w3cdtf">2013</copyrightDate></originInfo><language><languageTerm type="code" authority="rfc3066">en</languageTerm><languageTerm type="code" authority="iso639-2b">eng</languageTerm></language><abstract lang="en">Abstract: We review the information-geometric framework for statistical pattern recognition: First, we explain the role of statistical similarity measures and distances in fundamental statistical pattern recognition problems. We then concisely review the main statistical distances and report a novel versatile family of divergences. Depending on their intrinsic complexity, the statistical patterns are learned by either atomic parametric distributions, semi-parametric finite mixtures, or non-parametric kernel density distributions. Those statistical patterns are interpreted and handled geometrically in statistical manifolds either as single points, weighted sparse point sets or non-weighted dense point sets. We explain the construction of the two prominent families of statistical manifolds: The Rao Riemannian manifolds with geodesic metric distances, and the Amari-Chentsov manifolds with dual asymmetric non-metric divergences. For the latter manifolds, when considering atomic distributions from the same exponential families (including the ubiquitous Gaussian and multinomial families), we end up with dually flat exponential family manifolds that play a crucial role in many applications. We compare the advantages and disadvantages of these two approaches from the algorithmic point of view. Finally, we conclude with further perspectives on how “geometric thinking” may spur novel pattern modeling and processing paradigms.</abstract><subject lang="en"><genre>Keywords</genre><topic>Statistical manifolds</topic><topic>mixture modeling</topic><topic>kernel density estimator</topic><topic>exponential families</topic><topic>clustering</topic><topic>Voronoi diagrams</topic></subject><relatedItem type="host"><titleInfo><title>Similarity-Based Pattern Recognition</title><subTitle>Second International Workshop, SIMBAD 2013, York, UK, July 3-5, 2013. Proceedings</subTitle></titleInfo><name type="personal"><namePart type="given">Edwin</namePart><namePart type="family">Hancock</namePart><affiliation>Department of Computer Science, University of York, Deramore Lane, YO10 5GH, York, UK</affiliation><affiliation>E-mail: erh@cs.york.ac.uk</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Marcello</namePart><namePart type="family">Pelillo</namePart><affiliation>DAIS, Università Ca’ Foscari, Via Torino 155, 30172, Venice, Italy</affiliation><affiliation>E-mail: pelillo@dsi.unive.it</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><genre type="book-series" authority="ISTEX" authorityURI="https://publication-type.data.istex.fr" valueURI="https://publication-type.data.istex.fr/ark:/67375/JMC-0G6R5W5T-Z">book-series</genre><originInfo><publisher>Springer</publisher><copyrightDate encoding="w3cdtf">2013</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="SCI">Computer Science</topic><topic authority="SpringerSubjectCodes" authorityURI="SCI2203X">Pattern Recognition</topic><topic authority="SpringerSubjectCodes" authorityURI="SCI22021">Image Processing and Computer Vision</topic><topic authority="SpringerSubjectCodes" authorityURI="SCI21017">Artificial Intelligence (incl. Robotics)</topic><topic authority="SpringerSubjectCodes" authorityURI="SCI18024">Database Management</topic><topic authority="SpringerSubjectCodes" authorityURI="SCI16021">Algorithm Analysis and Problem Complexity</topic><topic authority="SpringerSubjectCodes" authorityURI="SCI18040">Information Systems Applications (incl. Internet)</topic></subject><identifier type="DOI">10.1007/978-3-642-39140-8</identifier><identifier type="ISBN">978-3-642-39139-2</identifier><identifier type="eISBN">978-3-642-39140-8</identifier><identifier type="ISSN">0302-9743</identifier><identifier type="eISSN">1611-3349</identifier><identifier type="BookTitleID">316609</identifier><identifier type="BookID">978-3-642-39140-8</identifier><identifier type="BookChapterCount">19</identifier><identifier type="BookVolumeNumber">7953</identifier><identifier type="BookSequenceNumber">7953</identifier><part><date>2013</date><detail type="volume"><number>7953</number><caption>vol.</caption></detail><extent unit="pages"><start>1</start><end>25</end></extent></part><recordInfo><recordOrigin>Springer-Verlag Berlin Heidelberg, 2013</recordOrigin></recordInfo></relatedItem><relatedItem type="series"><titleInfo><title>Lecture Notes in Computer Science</title></titleInfo><name type="personal"><namePart type="given">David</namePart><namePart type="family">Hutchison</namePart><affiliation>Lancaster University, Lancaster, UK</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Takeo</namePart><namePart type="family">Kanade</namePart><affiliation>Carnegie Mellon University, Pittsburgh, PA, USA</affiliation><role><roleTerm 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 type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Moni</namePart><namePart type="family">Naor</namePart><affiliation>Weizmann Institute of Science, Rehovot, Israel</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Oscar</namePart><namePart type="family">Nierstrasz</namePart><affiliation>University of Bern, Bern, Switzerland</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">C.</namePart><namePart type="family">Pandu Rangan</namePart><affiliation>Indian Institute of Technology, Madras, India</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Bernhard</namePart><namePart type="family">Steffen</namePart><affiliation>University of Dortmund, Dortmund, Germany</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Madhu</namePart><namePart type="family">Sudan</namePart><affiliation>Massachusetts Institute of Technology, MA, USA</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Demetri</namePart><namePart type="family">Terzopoulos</namePart><affiliation>University of California, Los Angeles, CA, USA</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Doug</namePart><namePart type="family">Tygar</namePart><affiliation>University of California, Berkeley, CA, 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, Saarbrücken, Germany</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><originInfo><publisher>Springer</publisher><copyrightDate encoding="w3cdtf">2013</copyrightDate><issuance>serial</issuance></originInfo><identifier type="ISSN">0302-9743</identifier><identifier type="eISSN">1611-3349</identifier><identifier type="SeriesID">558</identifier><recordInfo><recordOrigin>Springer-Verlag Berlin Heidelberg, 2013</recordOrigin></recordInfo></relatedItem><identifier type="istex">77ECD0DEF000EEEF43CA1A6F4C4FEC696F8A1ABA</identifier><identifier type="ark">ark:/67375/HCB-51Z96TLC-5</identifier><identifier type="DOI">10.1007/978-3-642-39140-8_1</identifier><identifier type="ChapterID">1</identifier><identifier type="ChapterID">Chap1</identifier><accessCondition type="use and reproduction" contentType="copyright">Springer-Verlag Berlin Heidelberg, 2013</accessCondition><recordInfo><recordContentSource authority="ISTEX" authorityURI="https://loaded-corpus.data.istex.fr" valueURI="https://loaded-corpus.data.istex.fr/ark:/67375/XBH-RLRX46XW-4">springer</recordContentSource><recordOrigin>Springer-Verlag Berlin Heidelberg, 2013</recordOrigin></recordInfo></mods><json:item><extension>json</extension><original>false</original><mimetype>application/json</mimetype><uri>https://api.istex.fr/ark:/67375/HCB-51Z96TLC-5/record.json</uri></json:item></metadata><annexes><json:item><extension>xml</extension><original>true</original><mimetype>application/xml</mimetype><uri>https://api.istex.fr/ark:/67375/HCB-51Z96TLC-5/annexes.xml</uri></json:item></annexes></istex></record>Pour manipuler ce document sous Unix (Dilib)
EXPLOR_STEP=$WICRI_ROOT/Wicri/Lorraine/explor/InforLorV4/Data/Istex/Corpus
HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 001B74 | SxmlIndent | more
Ou
HfdSelect -h $EXPLOR_AREA/Data/Istex/Corpus/biblio.hfd -nk 001B74 | SxmlIndent | more
Pour mettre un lien sur cette page dans le réseau Wicri
{{Explor lien
|wiki= Wicri/Lorraine
|area= InforLorV4
|flux= Istex
|étape= Corpus
|type= RBID
|clé= ISTEX:77ECD0DEF000EEEF43CA1A6F4C4FEC696F8A1ABA
|texte= Pattern Learning and Recognition on Statistical Manifolds: An Information-Geometric Review
}}
| This area was generated with Dilib version V0.6.33. |  |