Diego Esteves, Diego Moussallem, Tommaso Soru, C. Baron, Jens Lehmann, A. N. Ngomo, J. C. Duarte
{"title":"一个导出机器学习实验的库","authors":"Diego Esteves, Diego Moussallem, Tommaso Soru, C. Baron, Jens Lehmann, A. N. Ngomo, J. C. Duarte","doi":"10.1145/3106426.3106530","DOIUrl":null,"url":null,"abstract":"A choice of the best computational solution for a particular task is increasingly reliant on experimentation. Even though experiments are often described through text, tables, and figures, their descriptions are often incomplete or confusing. Thus, researchers often have to perform lengthy web searches for reproducing and understanding the results. In order to minimize this gap, vocabularies and ontologies have been proposed for representing data mining and machine learning (ML) experiments. However, we still lack proper tools to export properly these metadata. To this end, we present an open-source library dubbed LOG4MEX which aims at supporting the scientific community to fulfill this gap.","PeriodicalId":20685,"journal":{"name":"Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics","volume":"76 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2017-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"LOG4MEX: a library to export machine learning experiments\",\"authors\":\"Diego Esteves, Diego Moussallem, Tommaso Soru, C. Baron, Jens Lehmann, A. N. Ngomo, J. C. Duarte\",\"doi\":\"10.1145/3106426.3106530\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A choice of the best computational solution for a particular task is increasingly reliant on experimentation. Even though experiments are often described through text, tables, and figures, their descriptions are often incomplete or confusing. Thus, researchers often have to perform lengthy web searches for reproducing and understanding the results. In order to minimize this gap, vocabularies and ontologies have been proposed for representing data mining and machine learning (ML) experiments. However, we still lack proper tools to export properly these metadata. To this end, we present an open-source library dubbed LOG4MEX which aims at supporting the scientific community to fulfill this gap.\",\"PeriodicalId\":20685,\"journal\":{\"name\":\"Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics\",\"volume\":\"76 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-08-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3106426.3106530\",\"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 the 7th International Conference on Web Intelligence, Mining and Semantics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3106426.3106530","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
LOG4MEX: a library to export machine learning experiments
A choice of the best computational solution for a particular task is increasingly reliant on experimentation. Even though experiments are often described through text, tables, and figures, their descriptions are often incomplete or confusing. Thus, researchers often have to perform lengthy web searches for reproducing and understanding the results. In order to minimize this gap, vocabularies and ontologies have been proposed for representing data mining and machine learning (ML) experiments. However, we still lack proper tools to export properly these metadata. To this end, we present an open-source library dubbed LOG4MEX which aims at supporting the scientific community to fulfill this gap.