{"title":"GSN/sup /神经网络的训练算法","authors":"A. de Carvalho, D. Bisset, M. Fairhurst","doi":"10.1109/CYBVIS.1996.629443","DOIUrl":null,"url":null,"abstract":"This paper presents and analyses distinct learning strategies which have been used by GSN/sup f/ architectures. Sharing the common feature of being one-shot learning, these strategies achieve different performances as key parameters are changed. These algorithms are evaluated against each other by taking into account the training time, saturation rates, learning conflict rates and recognition performance.","PeriodicalId":103287,"journal":{"name":"Proceedings II Workshop on Cybernetic Vision","volume":"22 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1996-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Training algorithms for GSN/sup f/ neural networks\",\"authors\":\"A. de Carvalho, D. Bisset, M. Fairhurst\",\"doi\":\"10.1109/CYBVIS.1996.629443\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents and analyses distinct learning strategies which have been used by GSN/sup f/ architectures. Sharing the common feature of being one-shot learning, these strategies achieve different performances as key parameters are changed. These algorithms are evaluated against each other by taking into account the training time, saturation rates, learning conflict rates and recognition performance.\",\"PeriodicalId\":103287,\"journal\":{\"name\":\"Proceedings II Workshop on Cybernetic Vision\",\"volume\":\"22 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1996-12-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings II Workshop on Cybernetic Vision\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CYBVIS.1996.629443\",\"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 II Workshop on Cybernetic Vision","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CYBVIS.1996.629443","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Training algorithms for GSN/sup f/ neural networks
This paper presents and analyses distinct learning strategies which have been used by GSN/sup f/ architectures. Sharing the common feature of being one-shot learning, these strategies achieve different performances as key parameters are changed. These algorithms are evaluated against each other by taking into account the training time, saturation rates, learning conflict rates and recognition performance.
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