{"title":"基于多核学习的模式匹配人脸识别研究","authors":"Shan Feng","doi":"10.2174/1874444301507011796","DOIUrl":null,"url":null,"abstract":"The paper analyses the multiple kernel learning-based face recognition in pattern matching area. Based on the analysis of the basic theory of multiple kernel SVM, this thesis focuses on the multiple kernel SVM algorithm based on semi-infinite linear program (SILP), including SILP based on column generation (CG) and SILP based on chunking algo- rithm (CA). The two SILP improved algorithms are applied to several classification problems, including UCI binary clas- sification problem datasets and multi-classification problem datasets. Furthermore, the two SILP improved algorithms are applied to the actual problems of face recognition. The experiment data shows that with the multiple kernel learning-based method, the performance of face recognition can be obviously improved.","PeriodicalId":153592,"journal":{"name":"The Open Automation and Control Systems Journal","volume":"20 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"The Research on the Multiple Kernel Learning-based Face Recognition inPattern Matching\",\"authors\":\"Shan Feng\",\"doi\":\"10.2174/1874444301507011796\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The paper analyses the multiple kernel learning-based face recognition in pattern matching area. Based on the analysis of the basic theory of multiple kernel SVM, this thesis focuses on the multiple kernel SVM algorithm based on semi-infinite linear program (SILP), including SILP based on column generation (CG) and SILP based on chunking algo- rithm (CA). The two SILP improved algorithms are applied to several classification problems, including UCI binary clas- sification problem datasets and multi-classification problem datasets. Furthermore, the two SILP improved algorithms are applied to the actual problems of face recognition. The experiment data shows that with the multiple kernel learning-based method, the performance of face recognition can be obviously improved.\",\"PeriodicalId\":153592,\"journal\":{\"name\":\"The Open Automation and Control Systems Journal\",\"volume\":\"20 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-10-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"The Open Automation and Control Systems Journal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2174/1874444301507011796\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"The Open Automation and Control Systems Journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2174/1874444301507011796","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The Research on the Multiple Kernel Learning-based Face Recognition inPattern Matching
The paper analyses the multiple kernel learning-based face recognition in pattern matching area. Based on the analysis of the basic theory of multiple kernel SVM, this thesis focuses on the multiple kernel SVM algorithm based on semi-infinite linear program (SILP), including SILP based on column generation (CG) and SILP based on chunking algo- rithm (CA). The two SILP improved algorithms are applied to several classification problems, including UCI binary clas- sification problem datasets and multi-classification problem datasets. Furthermore, the two SILP improved algorithms are applied to the actual problems of face recognition. The experiment data shows that with the multiple kernel learning-based method, the performance of face recognition can be obviously improved.