{"title":"前馈神经网络的惩罚OBS方案","authors":"Jiang Meng","doi":"10.1109/ICTAI.2005.93","DOIUrl":null,"url":null,"abstract":"This paper presented a new scheme called penalty OBS (optimal brain surgeon) for the feedforward neural network learning. The penalty OBS scheme takes OBS pruning case as a penalty item of the network cost function, and develops two applied methods based on the common algorithms of network learning. As a novel revision of OBS, the new scheme not only saves the runtime to calculate Hessian matrix after training, but also improves the generalization a lot and keeps over-linear rapidity of convergence. The simulating results verified the advantages","PeriodicalId":294694,"journal":{"name":"17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2005-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"Penalty OBS scheme for feedforward neural network\",\"authors\":\"Jiang Meng\",\"doi\":\"10.1109/ICTAI.2005.93\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presented a new scheme called penalty OBS (optimal brain surgeon) for the feedforward neural network learning. The penalty OBS scheme takes OBS pruning case as a penalty item of the network cost function, and develops two applied methods based on the common algorithms of network learning. As a novel revision of OBS, the new scheme not only saves the runtime to calculate Hessian matrix after training, but also improves the generalization a lot and keeps over-linear rapidity of convergence. The simulating results verified the advantages\",\"PeriodicalId\":294694,\"journal\":{\"name\":\"17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2005-11-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICTAI.2005.93\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICTAI.2005.93","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
This paper presented a new scheme called penalty OBS (optimal brain surgeon) for the feedforward neural network learning. The penalty OBS scheme takes OBS pruning case as a penalty item of the network cost function, and develops two applied methods based on the common algorithms of network learning. As a novel revision of OBS, the new scheme not only saves the runtime to calculate Hessian matrix after training, but also improves the generalization a lot and keeps over-linear rapidity of convergence. The simulating results verified the advantages