{"title":"EDA中的数据挖掘——基本原则、承诺和约束","authors":"Li-C. Wang, M. Abadir","doi":"10.1145/2593069.2596675","DOIUrl":null,"url":null,"abstract":"This paper discusses the basic principles of applying data mining in Electronic Design Automation. It begins by introducing several important concepts in statistical learning and summarizes different types of learning algorithms. Then, the experience of developing a practical data mining application is described, including promises that are demonstrated through positive results based on industrial settings and constraints explained in their respective application contexts.","PeriodicalId":433816,"journal":{"name":"2014 51st ACM/EDAC/IEEE Design Automation Conference (DAC)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"19","resultStr":"{\"title\":\"Data mining in EDA - Basic principles, promises, and constraints\",\"authors\":\"Li-C. Wang, M. Abadir\",\"doi\":\"10.1145/2593069.2596675\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper discusses the basic principles of applying data mining in Electronic Design Automation. It begins by introducing several important concepts in statistical learning and summarizes different types of learning algorithms. Then, the experience of developing a practical data mining application is described, including promises that are demonstrated through positive results based on industrial settings and constraints explained in their respective application contexts.\",\"PeriodicalId\":433816,\"journal\":{\"name\":\"2014 51st ACM/EDAC/IEEE Design Automation Conference (DAC)\",\"volume\":\"32 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"19\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 51st ACM/EDAC/IEEE Design Automation Conference (DAC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2593069.2596675\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 51st ACM/EDAC/IEEE Design Automation Conference (DAC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2593069.2596675","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Data mining in EDA - Basic principles, promises, and constraints
This paper discusses the basic principles of applying data mining in Electronic Design Automation. It begins by introducing several important concepts in statistical learning and summarizes different types of learning algorithms. Then, the experience of developing a practical data mining application is described, including promises that are demonstrated through positive results based on industrial settings and constraints explained in their respective application contexts.