{"title":"用新信息更新但规模有限的经验数据集的智能分析的一些特征","authors":"M. I. Zabezhailo, A. V. Amentes","doi":"10.3103/S0005105523030093","DOIUrl":null,"url":null,"abstract":"<p>This paper discusses certain possibilities and limitations of the use of mathematical models and methods of computer data analysis in the processing of collections of empirical data, which are open, replenished with new elements but limited in size. The characteristics of the statistical methods of data analysis, artificial neural networks, and methods based on interpolation-extrapolation techniques for identifying empirical cause-and-effect dependencies hidden in the analyzed data are considered.</p>","PeriodicalId":42995,"journal":{"name":"AUTOMATIC DOCUMENTATION AND MATHEMATICAL LINGUISTICS","volume":null,"pages":null},"PeriodicalIF":0.5000,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Some Features of Intelligent Analysis of Empirical Data Collections Updated with New Information, but Limited in Size\",\"authors\":\"M. I. Zabezhailo, A. V. Amentes\",\"doi\":\"10.3103/S0005105523030093\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>This paper discusses certain possibilities and limitations of the use of mathematical models and methods of computer data analysis in the processing of collections of empirical data, which are open, replenished with new elements but limited in size. The characteristics of the statistical methods of data analysis, artificial neural networks, and methods based on interpolation-extrapolation techniques for identifying empirical cause-and-effect dependencies hidden in the analyzed data are considered.</p>\",\"PeriodicalId\":42995,\"journal\":{\"name\":\"AUTOMATIC DOCUMENTATION AND MATHEMATICAL LINGUISTICS\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.5000,\"publicationDate\":\"2023-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"AUTOMATIC DOCUMENTATION AND MATHEMATICAL LINGUISTICS\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://link.springer.com/article/10.3103/S0005105523030093\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"AUTOMATIC DOCUMENTATION AND MATHEMATICAL LINGUISTICS","FirstCategoryId":"1085","ListUrlMain":"https://link.springer.com/article/10.3103/S0005105523030093","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
Some Features of Intelligent Analysis of Empirical Data Collections Updated with New Information, but Limited in Size
This paper discusses certain possibilities and limitations of the use of mathematical models and methods of computer data analysis in the processing of collections of empirical data, which are open, replenished with new elements but limited in size. The characteristics of the statistical methods of data analysis, artificial neural networks, and methods based on interpolation-extrapolation techniques for identifying empirical cause-and-effect dependencies hidden in the analyzed data are considered.
期刊介绍：
Automatic Documentation and Mathematical Linguistics is an international peer reviewed journal that covers all aspects of automation of information processes and systems, as well as algorithms and methods for automatic language analysis. Emphasis is on the practical applications of new technologies and techniques for information analysis and processing.
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