Jinglong Fang, Shuo Chen, Zhigeng Pan, Yigang Wang
{"title":"支持向量机二次训练的简化","authors":"Jinglong Fang, Shuo Chen, Zhigeng Pan, Yigang Wang","doi":"10.1109/ICAT.2006.27","DOIUrl":null,"url":null,"abstract":"For complicated recognition problem, the number of support vectors is large and recognition speed is low, because some sample were divided into section by error this time. To solve this problem, a method is bought to simplify the support vector machines based the minimal misestimate margin idea. Experiments show that this new support vector machine not only reduces the number of support vectors and recognition time but also has the same accuracy as (even better than) traditional support vector machine.","PeriodicalId":133842,"journal":{"name":"16th International Conference on Artificial Reality and Telexistence--Workshops (ICAT'06)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"A Simplification to Support Vector Machine for the Second Training\",\"authors\":\"Jinglong Fang, Shuo Chen, Zhigeng Pan, Yigang Wang\",\"doi\":\"10.1109/ICAT.2006.27\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"For complicated recognition problem, the number of support vectors is large and recognition speed is low, because some sample were divided into section by error this time. To solve this problem, a method is bought to simplify the support vector machines based the minimal misestimate margin idea. Experiments show that this new support vector machine not only reduces the number of support vectors and recognition time but also has the same accuracy as (even better than) traditional support vector machine.\",\"PeriodicalId\":133842,\"journal\":{\"name\":\"16th International Conference on Artificial Reality and Telexistence--Workshops (ICAT'06)\",\"volume\":\"14 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2006-11-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"16th International Conference on Artificial Reality and Telexistence--Workshops (ICAT'06)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICAT.2006.27\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"16th International Conference on Artificial Reality and Telexistence--Workshops (ICAT'06)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICAT.2006.27","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Simplification to Support Vector Machine for the Second Training
For complicated recognition problem, the number of support vectors is large and recognition speed is low, because some sample were divided into section by error this time. To solve this problem, a method is bought to simplify the support vector machines based the minimal misestimate margin idea. Experiments show that this new support vector machine not only reduces the number of support vectors and recognition time but also has the same accuracy as (even better than) traditional support vector machine.