{"title":"关于核函数的可分性","authors":"Tao Wu, Hangen He, D. Hu","doi":"10.1109/ICONIP.2002.1198220","DOIUrl":null,"url":null,"abstract":"How to select a kernel function for the given data is an open problem in the research of support vector machine (SVM). There is a question puzzling many people: suppose the training data are separated nonlinearly in the input space, how do we know that the chosen kernel function can make the training data to be separated linearly in the feature space? A simple method is presented to decide if a selected kernel function can separate the given data linearly or not in the feature space.","PeriodicalId":146553,"journal":{"name":"Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02.","volume":"15 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2002-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"On the separability of kernel functions\",\"authors\":\"Tao Wu, Hangen He, D. Hu\",\"doi\":\"10.1109/ICONIP.2002.1198220\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"How to select a kernel function for the given data is an open problem in the research of support vector machine (SVM). There is a question puzzling many people: suppose the training data are separated nonlinearly in the input space, how do we know that the chosen kernel function can make the training data to be separated linearly in the feature space? A simple method is presented to decide if a selected kernel function can separate the given data linearly or not in the feature space.\",\"PeriodicalId\":146553,\"journal\":{\"name\":\"Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02.\",\"volume\":\"15 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2002-11-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICONIP.2002.1198220\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICONIP.2002.1198220","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
How to select a kernel function for the given data is an open problem in the research of support vector machine (SVM). There is a question puzzling many people: suppose the training data are separated nonlinearly in the input space, how do we know that the chosen kernel function can make the training data to be separated linearly in the feature space? A simple method is presented to decide if a selected kernel function can separate the given data linearly or not in the feature space.
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