{"title":"随机学习机的预测误差","authors":"K. Ikeda, Noboru Murata, S. Amari","doi":"10.1109/ICNN.1994.374346","DOIUrl":null,"url":null,"abstract":"The more the number of training examples included, the better a learning machine will behave. It is an important to know how fast and how well the behavior is improved. The average prediction error is one of the most popular criteria to evaluate the behavior. We have regarded the machine learning from the point of view of parameter estimation and derived the average prediction error of stochastic dichotomy machines by the information geometrical method.<<ETX>>","PeriodicalId":209128,"journal":{"name":"Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94)","volume":"166 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1994-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Prediction error of stochastic learning machine\",\"authors\":\"K. Ikeda, Noboru Murata, S. Amari\",\"doi\":\"10.1109/ICNN.1994.374346\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The more the number of training examples included, the better a learning machine will behave. It is an important to know how fast and how well the behavior is improved. The average prediction error is one of the most popular criteria to evaluate the behavior. We have regarded the machine learning from the point of view of parameter estimation and derived the average prediction error of stochastic dichotomy machines by the information geometrical method.<<ETX>>\",\"PeriodicalId\":209128,\"journal\":{\"name\":\"Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94)\",\"volume\":\"166 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1994-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICNN.1994.374346\",\"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 1994 IEEE International Conference on Neural Networks (ICNN'94)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICNN.1994.374346","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The more the number of training examples included, the better a learning machine will behave. It is an important to know how fast and how well the behavior is improved. The average prediction error is one of the most popular criteria to evaluate the behavior. We have regarded the machine learning from the point of view of parameter estimation and derived the average prediction error of stochastic dichotomy machines by the information geometrical method.<>
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