{"title":"科学数据挖掘与StripMiner/sup TM/","authors":"M. Embrechts, F. Arciniegas, M. Ozdemir, M. Momma","doi":"10.1109/SMCIA.2001.936721","DOIUrl":null,"url":null,"abstract":"The paper introduces scientific data mining, the standard data-mining problem, and the strip-mining problem. StripMiner/sup TM/, a shell program for feature reduction and predictive modeling, integrates the executions of several different machine-learning models (partial least squares regression, genetic algorithms, support vector machines, neural networks, and local learning). This paper introduces the StripMiner/sup TM/ code, its functionality, and its options.","PeriodicalId":104202,"journal":{"name":"SMCia/01. Proceedings of the 2001 IEEE Mountain Workshop on Soft Computing in Industrial Applications (Cat. No.01EX504)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2001-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Scientific data mining with StripMiner/sup TM/\",\"authors\":\"M. Embrechts, F. Arciniegas, M. Ozdemir, M. Momma\",\"doi\":\"10.1109/SMCIA.2001.936721\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The paper introduces scientific data mining, the standard data-mining problem, and the strip-mining problem. StripMiner/sup TM/, a shell program for feature reduction and predictive modeling, integrates the executions of several different machine-learning models (partial least squares regression, genetic algorithms, support vector machines, neural networks, and local learning). This paper introduces the StripMiner/sup TM/ code, its functionality, and its options.\",\"PeriodicalId\":104202,\"journal\":{\"name\":\"SMCia/01. Proceedings of the 2001 IEEE Mountain Workshop on Soft Computing in Industrial Applications (Cat. No.01EX504)\",\"volume\":\"10 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2001-06-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"SMCia/01. Proceedings of the 2001 IEEE Mountain Workshop on Soft Computing in Industrial Applications (Cat. No.01EX504)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SMCIA.2001.936721\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"SMCia/01. Proceedings of the 2001 IEEE Mountain Workshop on Soft Computing in Industrial Applications (Cat. No.01EX504)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SMCIA.2001.936721","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The paper introduces scientific data mining, the standard data-mining problem, and the strip-mining problem. StripMiner/sup TM/, a shell program for feature reduction and predictive modeling, integrates the executions of several different machine-learning models (partial least squares regression, genetic algorithms, support vector machines, neural networks, and local learning). This paper introduces the StripMiner/sup TM/ code, its functionality, and its options.
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