A support vector machine-based pattern recognizer using selected features for control chart patterns analysis
In this paper we review two implementation modes of control chart pattern recognition and introduce a new research problem concerning pattern displacement problem in the ¿recognition only when necessary¿ mode. A set of features are developed by taking the pattern displacement into account. Simulatio...
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| creator | Cheng, C.S. Cheng, H.P. Huang, K.K. |
| description | In this paper we review two implementation modes of control chart pattern recognition and introduce a new research problem concerning pattern displacement problem in the ¿recognition only when necessary¿ mode. A set of features are developed by taking the pattern displacement into account. Simulation studies indicate that an SVM-based pattern recognizer with features as input vector performs significantly better than that of using raw data as inputs. |
| doi_str_mv | 10.1109/IEEM.2009.5373318 |
| format | Conference Proceeding |
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Simulation studies indicate that an SVM-based pattern recognizer with features as input vector performs significantly better than that of using raw data as inputs.</description><subject>Artificial neural networks</subject><subject>Control charts</subject><subject>Data mining</subject><subject>Feature extraction</subject><subject>features</subject><subject>Monitoring</subject><subject>Pattern analysis</subject><subject>Pattern recognition</subject><subject>Stability</subject><subject>Support vector machines</subject><subject>SVM</subject><subject>Testing</subject><issn>2157-3611</issn><issn>2157-362X</issn><isbn>1424448697</isbn><isbn>9781424448692</isbn><isbn>9781424448708</isbn><isbn>1424448700</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2009</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNo9kMtOwzAURM2jEm3pByA2_oEUPxI_llVVoFIRG5DYVY5z3QalSWQ7SOXrMSJidRczc3RnELqjZEkp0Q_bzeZlyQjRy4JLzqm6QAstFc1ZnudKEnWJpowWMuOCfVyh2SgILa__BUonaPbL0KRQRN6gRQifhBDKlGBaTNFphcPQ952P-Ats7Dw-GXusW8hKE6DCvYkRfIs92O7Q1t_g8RDq9oADNMmfHA5MHDwE7FLYdm30XYPt0STiGA7YtKY5hzrcookzTYDFeOfo_XHztn7Odq9P2_Vql1nGeMxklXowrQVxpSsooQK0TJUNNSUrQZcqT88bqpiiXLFS8qqyQjojiDaVK_kc3f9xawDY974-GX_ejzPyH0HAYdI</recordid><startdate>200912</startdate><enddate>200912</enddate><creator>Cheng, C.S.</creator><creator>Cheng, H.P.</creator><creator>Huang, K.K.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>200912</creationdate><title>A support vector machine-based pattern recognizer using selected features for control chart patterns analysis</title><author>Cheng, C.S. ; Cheng, H.P. ; Huang, K.K.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c223t-7d44829960fbf51016e97487a1ab2be9b84296a18281382b73ddc67fa609adfb3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2009</creationdate><topic>Artificial neural networks</topic><topic>Control charts</topic><topic>Data mining</topic><topic>Feature extraction</topic><topic>features</topic><topic>Monitoring</topic><topic>Pattern analysis</topic><topic>Pattern recognition</topic><topic>Stability</topic><topic>Support vector machines</topic><topic>SVM</topic><topic>Testing</topic><toplevel>online_resources</toplevel><creatorcontrib>Cheng, C.S.</creatorcontrib><creatorcontrib>Cheng, H.P.</creatorcontrib><creatorcontrib>Huang, K.K.</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>Cheng, C.S.</au><au>Cheng, H.P.</au><au>Huang, K.K.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>A support vector machine-based pattern recognizer using selected features for control chart patterns analysis</atitle><btitle>2009 IEEE International Conference on Industrial Engineering and Engineering Management</btitle><stitle>IEEM</stitle><date>2009-12</date><risdate>2009</risdate><spage>419</spage><epage>423</epage><pages>419-423</pages><issn>2157-3611</issn><eissn>2157-362X</eissn><isbn>1424448697</isbn><isbn>9781424448692</isbn><eisbn>9781424448708</eisbn><eisbn>1424448700</eisbn><abstract>In this paper we review two implementation modes of control chart pattern recognition and introduce a new research problem concerning pattern displacement problem in the ¿recognition only when necessary¿ mode. 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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Artificial neural networks Control charts Data mining Feature extraction features Monitoring Pattern analysis Pattern recognition Stability Support vector machines SVM Testing |
| title | A support vector machine-based pattern recognizer using selected features for control chart patterns analysis |
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