Improving the classification accuracy of the scanning n-tuple method
In this article, the application of the scanning n-tuple technique to classification tasks is studied. The performance of this technique is examined in a handwritten character recognition task where the accuracy is initially low. This task is employed as a case study for designing a general-purpose...
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| creator | Tambouratzis, G. |
| description | In this article, the application of the scanning n-tuple technique to classification tasks is studied. The performance of this technique is examined in a handwritten character recognition task where the accuracy is initially low. This task is employed as a case study for designing a general-purpose algorithm that improves the scanning n-tuple performance in hard classification tasks, by focusing on the characteristics of the pattern space. Experimental results indicate that the use of the algorithm results in a substantial improvement of the scanning n-tuple classification performance in comparison to previous results. This improvement is shown to be equivalent to that achieved by employing structural knowledge regarding the specific pattern space. |
| doi_str_mv | 10.1109/ICPR.2000.906254 |
| format | Conference Proceeding |
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The performance of this technique is examined in a handwritten character recognition task where the accuracy is initially low. This task is employed as a case study for designing a general-purpose algorithm that improves the scanning n-tuple performance in hard classification tasks, by focusing on the characteristics of the pattern space. Experimental results indicate that the use of the algorithm results in a substantial improvement of the scanning n-tuple classification performance in comparison to previous results. 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This improvement is shown to be equivalent to that achieved by employing structural knowledge regarding the specific pattern space.</description><subject>Algorithm design and analysis</subject><subject>Character recognition</subject><subject>Frequency</subject><subject>Handwriting recognition</subject><subject>Natural languages</subject><subject>Neural networks</subject><subject>Pattern recognition</subject><subject>Retina</subject><subject>Speech processing</subject><issn>1051-4651</issn><issn>2831-7475</issn><isbn>0769507506</isbn><isbn>9780769507507</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2000</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNp9jrsOgjAUQG98JIK6G6f-AHgLlMeMGtmMcSdNLVIDhVAw4e-Nj9npDOcMB2BD0aUUk12Wni-uh4hugqHHgglYXuxTJwoiNgUbozBhGDEMZ2BRZNQJQkYXYBvzQPTQZ7EF-6xuu-ap9J30pSSi4saoQgneq0YTLsTQcTGSpvhoI7jW71Y7_dBWktSyL5vbCuYFr4xc_7iE7fFwTU-OklLmbadq3o35d9L_K1_kbD0A</recordid><startdate>2000</startdate><enddate>2000</enddate><creator>Tambouratzis, G.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>2000</creationdate><title>Improving the classification accuracy of the scanning n-tuple method</title><author>Tambouratzis, G.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-ieee_primary_9062543</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2000</creationdate><topic>Algorithm design and analysis</topic><topic>Character recognition</topic><topic>Frequency</topic><topic>Handwriting recognition</topic><topic>Natural languages</topic><topic>Neural networks</topic><topic>Pattern recognition</topic><topic>Retina</topic><topic>Speech processing</topic><toplevel>online_resources</toplevel><creatorcontrib>Tambouratzis, G.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Tambouratzis, G.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Improving the classification accuracy of the scanning n-tuple method</atitle><btitle>Proceedings 15th International Conference on Pattern Recognition. ICPR-2000</btitle><stitle>ICPR</stitle><date>2000</date><risdate>2000</risdate><volume>2</volume><spage>1046</spage><epage>1049 vol.2</epage><pages>1046-1049 vol.2</pages><issn>1051-4651</issn><eissn>2831-7475</eissn><isbn>0769507506</isbn><isbn>9780769507507</isbn><abstract>In this article, the application of the scanning n-tuple technique to classification tasks is studied. The performance of this technique is examined in a handwritten character recognition task where the accuracy is initially low. This task is employed as a case study for designing a general-purpose algorithm that improves the scanning n-tuple performance in hard classification tasks, by focusing on the characteristics of the pattern space. Experimental results indicate that the use of the algorithm results in a substantial improvement of the scanning n-tuple classification performance in comparison to previous results. 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| ispartof | Proceedings 15th International Conference on Pattern Recognition. ICPR-2000, 2000, Vol.2, p.1046-1049 vol.2 |
| issn | 1051-4651 2831-7475 |
| language | eng |
| recordid | cdi_ieee_primary_906254 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Algorithm design and analysis Character recognition Frequency Handwriting recognition Natural languages Neural networks Pattern recognition Retina Speech processing |
| title | Improving the classification accuracy of the scanning n-tuple method |
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