{"title":"Z-EDM算法的新进展","authors":"Luís M. Silva, J. M. D. Sá, Luís A. Alexandre","doi":"10.1109/ISDA.2006.204","DOIUrl":null,"url":null,"abstract":"In this paper we address some open questions on the recently proposed Zero-Error Density Maximization algorithm for MLP training. We propose a new version of the cost function that solves a training problem encountered in previous work and prove that the use of a nonparametric density estimator preserves the optimal solution. Some experiments are reported comparing this cost function to the usual mean-square error and cross entropy cost functions","PeriodicalId":116729,"journal":{"name":"Sixth International Conference on Intelligent Systems Design and Applications","volume":"74 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":"{\"title\":\"New developments of the Z-EDM algorithm\",\"authors\":\"Luís M. Silva, J. M. D. Sá, Luís A. Alexandre\",\"doi\":\"10.1109/ISDA.2006.204\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper we address some open questions on the recently proposed Zero-Error Density Maximization algorithm for MLP training. We propose a new version of the cost function that solves a training problem encountered in previous work and prove that the use of a nonparametric density estimator preserves the optimal solution. Some experiments are reported comparing this cost function to the usual mean-square error and cross entropy cost functions\",\"PeriodicalId\":116729,\"journal\":{\"name\":\"Sixth International Conference on Intelligent Systems Design and Applications\",\"volume\":\"74 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2006-10-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Sixth International Conference on Intelligent Systems Design and Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISDA.2006.204\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Sixth International Conference on Intelligent Systems Design and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISDA.2006.204","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
In this paper we address some open questions on the recently proposed Zero-Error Density Maximization algorithm for MLP training. We propose a new version of the cost function that solves a training problem encountered in previous work and prove that the use of a nonparametric density estimator preserves the optimal solution. Some experiments are reported comparing this cost function to the usual mean-square error and cross entropy cost functions
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