{"title":"Machine Learning To Support Technical Document Indexing, How To Measure The Accuracy?","authors":"H. Blondelle, J. Micaelli","doi":"10.3997/2214-4609.201803012","DOIUrl":null,"url":null,"abstract":"Using a machine learning systems, a set of seismic documents has been automatically indexed on 25 metadata. The hold-out methodology has been used to evaluate the accuracy of the models. Results and lessons learnt are discussed.","PeriodicalId":231338,"journal":{"name":"First EAGE/PESGB Workshop Machine Learning","volume":"24 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"First EAGE/PESGB Workshop Machine Learning","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3997/2214-4609.201803012","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Using a machine learning systems, a set of seismic documents has been automatically indexed on 25 metadata. The hold-out methodology has been used to evaluate the accuracy of the models. Results and lessons learnt are discussed.
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