Cooperative Clustering for Training SVMs
Support vector machines are currently very popular approaches to supervised learning. Unfortunately, the computational load for training and classification procedures increases drastically with size of the training data set. In this paper, a method called cooperative clustering is proposed. With thi...
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| creator | Tian, Shengfeng Mu, Shaomin Yin, Chuanhuan |
| description | Support vector machines are currently very popular approaches to supervised learning. Unfortunately, the computational load for training and classification procedures increases drastically with size of the training data set. In this paper, a method called cooperative clustering is proposed. With this procedure, the set of data points with pre-determined size near the border of two classes is determined. This small set of data points is taken as the set of support vectors. The training of support vector machine is performed on this set of data points. With this approach, training efficiency and classification efficiency are achieved with small effects on generalization performance. This approach can also be used to reduce the number of support vectors in regression problems. |
| doi_str_mv | 10.1007/11759966_141 |
| format | Conference Proceeding |
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Unfortunately, the computational load for training and classification procedures increases drastically with size of the training data set. In this paper, a method called cooperative clustering is proposed. With this procedure, the set of data points with pre-determined size near the border of two classes is determined. This small set of data points is taken as the set of support vectors. The training of support vector machine is performed on this set of data points. With this approach, training efficiency and classification efficiency are achieved with small effects on generalization performance. 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Unfortunately, the computational load for training and classification procedures increases drastically with size of the training data set. In this paper, a method called cooperative clustering is proposed. With this procedure, the set of data points with pre-determined size near the border of two classes is determined. This small set of data points is taken as the set of support vectors. The training of support vector machine is performed on this set of data points. With this approach, training efficiency and classification efficiency are achieved with small effects on generalization performance. This approach can also be used to reduce the number of support vectors in regression problems.</description><subject>Applied sciences</subject><subject>Artificial intelligence</subject><subject>Cluster Center</subject><subject>Computer science; control theory; systems</subject><subject>Data processing. List processing. 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Data processing</subject><subject>Regression Problem</subject><subject>Software</subject><subject>Support Vector</subject><subject>Support Vector Machine</subject><subject>Training Algorithm</subject><issn>0302-9743</issn><issn>1611-3349</issn><isbn>354034439X</isbn><isbn>9783540344391</isbn><isbn>3540344403</isbn><isbn>9783540344407</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2006</creationdate><recordtype>conference_proceeding</recordtype><recordid>eNpNUE1LAzEQjV_gWnvzB_QiiLA6k8lmm6MsfkHFg1W8hWk2kdW6uySt4L93S0Wcw5sZ3uPxeEKcIFwgQHmJWBbGaG1R4Y44okIBKTXArshQI-ZEyuz9EWRe90UGBDI3paJDMU7pHYYh1ECUibOq63ofedV8-Um1XKeVj037NgldnMwjN-3meXp5SMfiIPAy-fHvHonnm-t5dZfPHm_vq6tZ7qSmVU7BoTPkmRdc1DwNdag1SBjSgHF1kJICMgwXShdIo1PsSU-9Zlmr4GgkTre-PSfHyxC5dU2yfWw-OX5bNKaQJcpBd77VpX4T2Ee76LqPZBHspij7vyj6AQbzVUs</recordid><startdate>2006</startdate><enddate>2006</enddate><creator>Tian, Shengfeng</creator><creator>Mu, Shaomin</creator><creator>Yin, Chuanhuan</creator><general>Springer Berlin Heidelberg</general><general>Springer</general><scope>IQODW</scope></search><sort><creationdate>2006</creationdate><title>Cooperative Clustering for Training SVMs</title><author>Tian, Shengfeng ; Mu, Shaomin ; Yin, Chuanhuan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c263t-3fc1c93eaaba5da8fdfd602040309cdf223f1a0cdf12cf361c4ae368e6a2d4fc3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2006</creationdate><topic>Applied sciences</topic><topic>Artificial intelligence</topic><topic>Cluster Center</topic><topic>Computer science; control theory; systems</topic><topic>Data processing. List processing. Character string processing</topic><topic>Exact sciences and technology</topic><topic>Memory organisation. Data processing</topic><topic>Regression Problem</topic><topic>Software</topic><topic>Support Vector</topic><topic>Support Vector Machine</topic><topic>Training Algorithm</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Tian, Shengfeng</creatorcontrib><creatorcontrib>Mu, Shaomin</creatorcontrib><creatorcontrib>Yin, Chuanhuan</creatorcontrib><collection>Pascal-Francis</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Tian, Shengfeng</au><au>Mu, Shaomin</au><au>Yin, Chuanhuan</au><au>Zurada, Jacek M.</au><au>Lu, Bao-Liang</au><au>Yi, Zhang</au><au>Yin, Hujun</au><au>Wang, Jun</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Cooperative Clustering for Training SVMs</atitle><btitle>Advances in Neural Networks - ISNN 2006</btitle><date>2006</date><risdate>2006</risdate><spage>962</spage><epage>967</epage><pages>962-967</pages><issn>0302-9743</issn><eissn>1611-3349</eissn><isbn>354034439X</isbn><isbn>9783540344391</isbn><eisbn>3540344403</eisbn><eisbn>9783540344407</eisbn><abstract>Support vector machines are currently very popular approaches to supervised learning. Unfortunately, the computational load for training and classification procedures increases drastically with size of the training data set. In this paper, a method called cooperative clustering is proposed. With this procedure, the set of data points with pre-determined size near the border of two classes is determined. This small set of data points is taken as the set of support vectors. The training of support vector machine is performed on this set of data points. With this approach, training efficiency and classification efficiency are achieved with small effects on generalization performance. This approach can also be used to reduce the number of support vectors in regression problems.</abstract><cop>Berlin, Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/11759966_141</doi><tpages>6</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0302-9743 |
| ispartof | Advances in Neural Networks - ISNN 2006, 2006, p.962-967 |
| issn | 0302-9743 1611-3349 |
| language | eng |
| recordid | cdi_pascalfrancis_primary_19952712 |
| source | Springer Books |
| subjects | Applied sciences Artificial intelligence Cluster Center Computer science control theory systems Data processing. List processing. Character string processing Exact sciences and technology Memory organisation. Data processing Regression Problem Software Support Vector Support Vector Machine Training Algorithm |
| title | Cooperative Clustering for Training SVMs |
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