{"title":"数据时代的过拟合问题与过度训练:特别是人工神经网络","authors":"Imanol Bilbao, J. Bilbao","doi":"10.1109/INTELCIS.2017.8260032","DOIUrl":null,"url":null,"abstract":"When we try to classify a set of data or to create a model to a cloud of points, different techniques can be used. Among them, Artificial Neural Networks are nowadays reinvented with the peak of the Machine Learning, Big Data, etc. In the process to find the best classification and be sure on it, one of the biggest concerns that we can come up against is the problem of overfitting. In this paper, we analyze it and set out a case study.","PeriodicalId":321315,"journal":{"name":"2017 Eighth International Conference on Intelligent Computing and Information Systems (ICICIS)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"70","resultStr":"{\"title\":\"Overfitting problem and the over-training in the era of data: Particularly for Artificial Neural Networks\",\"authors\":\"Imanol Bilbao, J. Bilbao\",\"doi\":\"10.1109/INTELCIS.2017.8260032\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"When we try to classify a set of data or to create a model to a cloud of points, different techniques can be used. Among them, Artificial Neural Networks are nowadays reinvented with the peak of the Machine Learning, Big Data, etc. In the process to find the best classification and be sure on it, one of the biggest concerns that we can come up against is the problem of overfitting. In this paper, we analyze it and set out a case study.\",\"PeriodicalId\":321315,\"journal\":{\"name\":\"2017 Eighth International Conference on Intelligent Computing and Information Systems (ICICIS)\",\"volume\":\"70 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"70\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 Eighth International Conference on Intelligent Computing and Information Systems (ICICIS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/INTELCIS.2017.8260032\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 Eighth International Conference on Intelligent Computing and Information Systems (ICICIS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/INTELCIS.2017.8260032","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Overfitting problem and the over-training in the era of data: Particularly for Artificial Neural Networks
When we try to classify a set of data or to create a model to a cloud of points, different techniques can be used. Among them, Artificial Neural Networks are nowadays reinvented with the peak of the Machine Learning, Big Data, etc. In the process to find the best classification and be sure on it, one of the biggest concerns that we can come up against is the problem of overfitting. In this paper, we analyze it and set out a case study.
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