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Improved minimum squared error method for robust classification
In this paper, we improved the method of minimum squared error for robust classification by altering its classification rule. The minimum squared error method, is one of the methods to minimize the sum of the squared error between the output of the linear function and the desired output, which first...
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| creator | Fangzhi Zhu Rui Yan Yong Sun |
| description | In this paper, we improved the method of minimum squared error for robust classification by altering its classification rule. The minimum squared error method, is one of the methods to minimize the sum of the squared error between the output of the linear function and the desired output, which first obtains the mapping that can best transform the training sample into its class label and then exploits the obtained mapping to predict the class label of the test sample. However, the improved method classifies all the test examples by comparing the difference of predicted class labels of test examples and training examples. Through experiment in section 4, we draw a conclusion that the improved MSE (IMSE) method can reduce the classification error and get a better classification result. |
| doi_str_mv | 10.1109/CCIS.2014.7175705 |
| format | conference_proceeding |
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The minimum squared error method, is one of the methods to minimize the sum of the squared error between the output of the linear function and the desired output, which first obtains the mapping that can best transform the training sample into its class label and then exploits the obtained mapping to predict the class label of the test sample. However, the improved method classifies all the test examples by comparing the difference of predicted class labels of test examples and training examples. Through experiment in section 4, we draw a conclusion that the improved MSE (IMSE) method can reduce the classification error and get a better classification result.</description><identifier>ISSN: 2376-5933</identifier><identifier>ISBN: 1479947202</identifier><identifier>ISBN: 9781479947201</identifier><identifier>EISSN: 2376-595X</identifier><identifier>EISBN: 1479944394</identifier><identifier>EISBN: 9781479947195</identifier><identifier>EISBN: 1479947199</identifier><identifier>EISBN: 9781479944392</identifier><identifier>DOI: 10.1109/CCIS.2014.7175705</identifier><language>eng</language><publisher>IEEE</publisher><subject>Algorithm design and analysis ; Classification algorithms ; Face recognition ; Image recognition ; Image resolution ; Minimum squared error ; Pattern recognition ; Robustness</subject><ispartof>2014 IEEE 3rd International Conference on Cloud Computing and Intelligence Systems, 2014, p.71-75</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/7175705$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,23930,23931,25140,27925,54555,54932</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/7175705$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Fangzhi Zhu</creatorcontrib><creatorcontrib>Rui Yan</creatorcontrib><creatorcontrib>Yong Sun</creatorcontrib><title>Improved minimum squared error method for robust classification</title><title>2014 IEEE 3rd International Conference on Cloud Computing and Intelligence Systems</title><addtitle>CCIS</addtitle><description>In this paper, we improved the method of minimum squared error for robust classification by altering its classification rule. The minimum squared error method, is one of the methods to minimize the sum of the squared error between the output of the linear function and the desired output, which first obtains the mapping that can best transform the training sample into its class label and then exploits the obtained mapping to predict the class label of the test sample. However, the improved method classifies all the test examples by comparing the difference of predicted class labels of test examples and training examples. Through experiment in section 4, we draw a conclusion that the improved MSE (IMSE) method can reduce the classification error and get a better classification result.</description><subject>Algorithm design and analysis</subject><subject>Classification algorithms</subject><subject>Face recognition</subject><subject>Image recognition</subject><subject>Image resolution</subject><subject>Minimum squared error</subject><subject>Pattern recognition</subject><subject>Robustness</subject><issn>2376-5933</issn><issn>2376-595X</issn><isbn>1479947202</isbn><isbn>9781479947201</isbn><isbn>1479944394</isbn><isbn>9781479947195</isbn><isbn>1479947199</isbn><isbn>9781479944392</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2014</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNo9UMtKxDAUjS9wHOcDxE1_oONN0jzuSqSMWhhwoYK7IUkTjEynY9IK_r0FR1fnyVkcQq4oLCkFvKnr5nnJgFZLRZVQII7IBa0UYlVxrI7JjHElS4Hi7eQvUAzY6X_A-TlZ5PwBABQlMs1m5Lbp9qn_8m3RxV3sxq7In6NJk_Yp9ano_PDet0WYaOrtmIfCbU3OMURnhtjvLslZMNvsFweck9f71Uv9WK6fHpr6bl1GpvRQSmvBCmgto-ACl5NprcZgNfWAzikmJUMQwvhKW8UNau6CVVPdYEDL5-T6dzd67zf7FDuTvjeHI_gPpaxOEg</recordid><startdate>20140101</startdate><enddate>20140101</enddate><creator>Fangzhi Zhu</creator><creator>Rui Yan</creator><creator>Yong Sun</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>20140101</creationdate><title>Improved minimum squared error method for robust classification</title><author>Fangzhi Zhu ; Rui Yan ; Yong Sun</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i278t-6bb0b50db210cf36278bb89fb81e09cc726629055ae48b73a983cfb7210a9f9b3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2014</creationdate><topic>Algorithm design and analysis</topic><topic>Classification algorithms</topic><topic>Face recognition</topic><topic>Image recognition</topic><topic>Image resolution</topic><topic>Minimum squared error</topic><topic>Pattern recognition</topic><topic>Robustness</topic><toplevel>online_resources</toplevel><creatorcontrib>Fangzhi Zhu</creatorcontrib><creatorcontrib>Rui Yan</creatorcontrib><creatorcontrib>Yong Sun</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</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>Fangzhi Zhu</au><au>Rui Yan</au><au>Yong Sun</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Improved minimum squared error method for robust classification</atitle><btitle>2014 IEEE 3rd International Conference on Cloud Computing and Intelligence Systems</btitle><stitle>CCIS</stitle><date>2014-01-01</date><risdate>2014</risdate><spage>71</spage><epage>75</epage><pages>71-75</pages><issn>2376-5933</issn><eissn>2376-595X</eissn><isbn>1479947202</isbn><isbn>9781479947201</isbn><eisbn>1479944394</eisbn><eisbn>9781479947195</eisbn><eisbn>1479947199</eisbn><eisbn>9781479944392</eisbn><abstract>In this paper, we improved the method of minimum squared error for robust classification by altering its classification rule. The minimum squared error method, is one of the methods to minimize the sum of the squared error between the output of the linear function and the desired output, which first obtains the mapping that can best transform the training sample into its class label and then exploits the obtained mapping to predict the class label of the test sample. However, the improved method classifies all the test examples by comparing the difference of predicted class labels of test examples and training examples. Through experiment in section 4, we draw a conclusion that the improved MSE (IMSE) method can reduce the classification error and get a better classification result.</abstract><pub>IEEE</pub><doi>10.1109/CCIS.2014.7175705</doi><tpages>5</tpages></addata></record> |
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| ispartof | 2014 IEEE 3rd International Conference on Cloud Computing and Intelligence Systems, 2014, p.71-75 |
| issn | 2376-5933 2376-595X |
| language | eng |
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| source | IEEE Xplore All Conference Series |
| subjects | Algorithm design and analysis Classification algorithms Face recognition Image recognition Image resolution Minimum squared error Pattern recognition Robustness |
| title | Improved minimum squared error method for robust classification |
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