{"title":"反向传播与广义回归遗传神经网络模型的比较。","authors":"P P Mager, R Reinhardt","doi":"","DOIUrl":null,"url":null,"abstract":"<p><p>The results of the backpropagation (BP) and generalized-regression genetic-neural (GRGN) network were compared using a series of nonpeptide arginine vasopressin VI antagonists. It was shown that both approaches are equivalent with respect to the recognition process while the BP network is superior over GRGN if the sample sizes are lowered by cross-validation.</p>","PeriodicalId":11297,"journal":{"name":"Drug design and discovery","volume":"16 1","pages":"49-53"},"PeriodicalIF":0.0000,"publicationDate":"1999-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A comparison of backpropagation and generalized-regression genetic-neural network models.\",\"authors\":\"P P Mager, R Reinhardt\",\"doi\":\"\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>The results of the backpropagation (BP) and generalized-regression genetic-neural (GRGN) network were compared using a series of nonpeptide arginine vasopressin VI antagonists. It was shown that both approaches are equivalent with respect to the recognition process while the BP network is superior over GRGN if the sample sizes are lowered by cross-validation.</p>\",\"PeriodicalId\":11297,\"journal\":{\"name\":\"Drug design and discovery\",\"volume\":\"16 1\",\"pages\":\"49-53\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1999-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Drug design and discovery\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Drug design and discovery","FirstCategoryId":"1085","ListUrlMain":"","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A comparison of backpropagation and generalized-regression genetic-neural network models.
The results of the backpropagation (BP) and generalized-regression genetic-neural (GRGN) network were compared using a series of nonpeptide arginine vasopressin VI antagonists. It was shown that both approaches are equivalent with respect to the recognition process while the BP network is superior over GRGN if the sample sizes are lowered by cross-validation.
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