A Theoretical and Empirical Study of a Noise-Tolerant Algorithm to Learn Geometric Patterns
Developing the ability to recognize a landmark from a visual image of a robot's current location is a fundamental problem in robotics. We describe a way in which the landmark matching problem can be mapped to that of learning a one-dimensional geometric pattern. The first contribution of our wo...
Gespeichert in:
| Veröffentlicht in: | Machine learning 1999-10, Vol.37 (1), p.5-49 |
|---|---|
| Hauptverfasser: | , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 49 |
|---|---|
| container_issue | 1 |
| container_start_page | 5 |
| container_title | Machine learning |
| container_volume | 37 |
| creator | Goldman, Sally A Scott, Stephen D |
| description | Developing the ability to recognize a landmark from a visual image of a robot's current location is a fundamental problem in robotics. We describe a way in which the landmark matching problem can be mapped to that of learning a one-dimensional geometric pattern. The first contribution of our work is an efficient noise-tolerant algorithm (designed using the statistical query model) to PAC learn the class of one-dimensional geometric patterns. The second contribution of our work is an empirical study of our algorithm that provides some evidence that statistical query algorithms may be valuable for use in practice for handling noisy data.[PUBLICATION ABSTRACT] |
| doi_str_mv | 10.1023/A:1007681724516 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest</sourceid><recordid>TN_cdi_proquest_miscellaneous_27188358</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>27188358</sourcerecordid><originalsourceid>FETCH-LOGICAL-c257t-1ceb0d7824d798877cd6d288d29252e1120d0585d04877ff1dd01c0ef834d0573</originalsourceid><addsrcrecordid>eNpdjr1PwzAUxC0EEqUws1oMbIH3nPijbFHVFqQKkCgTQ2XiF5oqiYvtDPz3RMDEdDrd707H2CXCDYLIb8s7BNDKoBaFRHXEJih1noFU8phNwBiZKRTylJ3FuAcAoYyasLeSb3bkA6Wmsi23veOL7tCEH_eSBvfFfc0tf_RNpGzjWwq2T7xsP3xo0q7jyfM12dDzFfmO0ljkzzYlCn08Zye1bSNd_OmUvS4Xm_l9tn5aPczLdVYJqVOGFb2D00YUTs-M0bpyygljnJgJKQhRgANppINiDOsanQOsgGqTF2Og8ym7_t09BP85UEzbrokVta3tyQ9xKzQak0szglf_wL0fQj9-22qpQaPKZ_k34MFgoA</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>757071639</pqid></control><display><type>article</type><title>A Theoretical and Empirical Study of a Noise-Tolerant Algorithm to Learn Geometric Patterns</title><source>Springer Nature - Complete Springer Journals</source><creator>Goldman, Sally A ; Scott, Stephen D</creator><creatorcontrib>Goldman, Sally A ; Scott, Stephen D</creatorcontrib><description>Developing the ability to recognize a landmark from a visual image of a robot's current location is a fundamental problem in robotics. We describe a way in which the landmark matching problem can be mapped to that of learning a one-dimensional geometric pattern. The first contribution of our work is an efficient noise-tolerant algorithm (designed using the statistical query model) to PAC learn the class of one-dimensional geometric patterns. The second contribution of our work is an empirical study of our algorithm that provides some evidence that statistical query algorithms may be valuable for use in practice for handling noisy data.[PUBLICATION ABSTRACT]</description><identifier>ISSN: 0885-6125</identifier><identifier>EISSN: 1573-0565</identifier><identifier>DOI: 10.1023/A:1007681724516</identifier><language>eng</language><publisher>Dordrecht: Springer Nature B.V</publisher><subject>Studies</subject><ispartof>Machine learning, 1999-10, Vol.37 (1), p.5-49</ispartof><rights>Kluwer Academic Publishers 1999</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c257t-1ceb0d7824d798877cd6d288d29252e1120d0585d04877ff1dd01c0ef834d0573</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27901,27902</link.rule.ids></links><search><creatorcontrib>Goldman, Sally A</creatorcontrib><creatorcontrib>Scott, Stephen D</creatorcontrib><title>A Theoretical and Empirical Study of a Noise-Tolerant Algorithm to Learn Geometric Patterns</title><title>Machine learning</title><description>Developing the ability to recognize a landmark from a visual image of a robot's current location is a fundamental problem in robotics. We describe a way in which the landmark matching problem can be mapped to that of learning a one-dimensional geometric pattern. The first contribution of our work is an efficient noise-tolerant algorithm (designed using the statistical query model) to PAC learn the class of one-dimensional geometric patterns. The second contribution of our work is an empirical study of our algorithm that provides some evidence that statistical query algorithms may be valuable for use in practice for handling noisy data.[PUBLICATION ABSTRACT]</description><subject>Studies</subject><issn>0885-6125</issn><issn>1573-0565</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>1999</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNpdjr1PwzAUxC0EEqUws1oMbIH3nPijbFHVFqQKkCgTQ2XiF5oqiYvtDPz3RMDEdDrd707H2CXCDYLIb8s7BNDKoBaFRHXEJih1noFU8phNwBiZKRTylJ3FuAcAoYyasLeSb3bkA6Wmsi23veOL7tCEH_eSBvfFfc0tf_RNpGzjWwq2T7xsP3xo0q7jyfM12dDzFfmO0ljkzzYlCn08Zye1bSNd_OmUvS4Xm_l9tn5aPczLdVYJqVOGFb2D00YUTs-M0bpyygljnJgJKQhRgANppINiDOsanQOsgGqTF2Og8ym7_t09BP85UEzbrokVta3tyQ9xKzQak0szglf_wL0fQj9-22qpQaPKZ_k34MFgoA</recordid><startdate>19991001</startdate><enddate>19991001</enddate><creator>Goldman, Sally A</creator><creator>Scott, Stephen D</creator><general>Springer Nature B.V</general><scope>3V.</scope><scope>7SC</scope><scope>7XB</scope><scope>88I</scope><scope>8AL</scope><scope>8AO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>M0N</scope><scope>M2P</scope><scope>P5Z</scope><scope>P62</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope></search><sort><creationdate>19991001</creationdate><title>A Theoretical and Empirical Study of a Noise-Tolerant Algorithm to Learn Geometric Patterns</title><author>Goldman, Sally A ; Scott, Stephen D</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c257t-1ceb0d7824d798877cd6d288d29252e1120d0585d04877ff1dd01c0ef834d0573</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>1999</creationdate><topic>Studies</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Goldman, Sally A</creatorcontrib><creatorcontrib>Scott, Stephen D</creatorcontrib><collection>ProQuest Central (Corporate)</collection><collection>Computer and Information Systems Abstracts</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Science Database (Alumni Edition)</collection><collection>Computing Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Computing Database</collection><collection>Science Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>ProQuest Central Basic</collection><jtitle>Machine learning</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Goldman, Sally A</au><au>Scott, Stephen D</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Theoretical and Empirical Study of a Noise-Tolerant Algorithm to Learn Geometric Patterns</atitle><jtitle>Machine learning</jtitle><date>1999-10-01</date><risdate>1999</risdate><volume>37</volume><issue>1</issue><spage>5</spage><epage>49</epage><pages>5-49</pages><issn>0885-6125</issn><eissn>1573-0565</eissn><abstract>Developing the ability to recognize a landmark from a visual image of a robot's current location is a fundamental problem in robotics. We describe a way in which the landmark matching problem can be mapped to that of learning a one-dimensional geometric pattern. The first contribution of our work is an efficient noise-tolerant algorithm (designed using the statistical query model) to PAC learn the class of one-dimensional geometric patterns. The second contribution of our work is an empirical study of our algorithm that provides some evidence that statistical query algorithms may be valuable for use in practice for handling noisy data.[PUBLICATION ABSTRACT]</abstract><cop>Dordrecht</cop><pub>Springer Nature B.V</pub><doi>10.1023/A:1007681724516</doi><tpages>45</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0885-6125 |
| ispartof | Machine learning, 1999-10, Vol.37 (1), p.5-49 |
| issn | 0885-6125 1573-0565 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_27188358 |
| source | Springer Nature - Complete Springer Journals |
| subjects | Studies |
| title | A Theoretical and Empirical Study of a Noise-Tolerant Algorithm to Learn Geometric Patterns |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-30T15%3A25%3A42IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=A%20Theoretical%20and%20Empirical%20Study%20of%20a%20Noise-Tolerant%20Algorithm%20to%20Learn%20Geometric%20Patterns&rft.jtitle=Machine%20learning&rft.au=Goldman,%20Sally%20A&rft.date=1999-10-01&rft.volume=37&rft.issue=1&rft.spage=5&rft.epage=49&rft.pages=5-49&rft.issn=0885-6125&rft.eissn=1573-0565&rft_id=info:doi/10.1023/A:1007681724516&rft_dat=%3Cproquest%3E27188358%3C/proquest%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=757071639&rft_id=info:pmid/&rfr_iscdi=true |