{"title":"混合动力汽车系统开发","authors":"","doi":"10.4018/978-1-7998-7316-7.ch011","DOIUrl":null,"url":null,"abstract":"This chapter presents the Hybrid-AutoML system requirements, design materials, model algorithms, and model design, which encompasses the design goals, architecture (a three-layered architecture), components, and characteristics of the Hybrid-AutoML toolkit developed in this research for automatic mode and model selection on single or multi-varying datasets. The mode components, decision learning and AutoProbClass unsupervised algorithms, and application API are described. The testing and evaluation of the model is conducted by two case studies.","PeriodicalId":134297,"journal":{"name":"Machine Learning in Cancer Research With Applications in Colon Cancer and Big Data Analysis","volume":"733 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Hybrid-AutoML System Development\",\"authors\":\"\",\"doi\":\"10.4018/978-1-7998-7316-7.ch011\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This chapter presents the Hybrid-AutoML system requirements, design materials, model algorithms, and model design, which encompasses the design goals, architecture (a three-layered architecture), components, and characteristics of the Hybrid-AutoML toolkit developed in this research for automatic mode and model selection on single or multi-varying datasets. The mode components, decision learning and AutoProbClass unsupervised algorithms, and application API are described. The testing and evaluation of the model is conducted by two case studies.\",\"PeriodicalId\":134297,\"journal\":{\"name\":\"Machine Learning in Cancer Research With Applications in Colon Cancer and Big Data Analysis\",\"volume\":\"733 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Machine Learning in Cancer Research With Applications in Colon Cancer and Big Data Analysis\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4018/978-1-7998-7316-7.ch011\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Machine Learning in Cancer Research With Applications in Colon Cancer and Big Data Analysis","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4018/978-1-7998-7316-7.ch011","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
This chapter presents the Hybrid-AutoML system requirements, design materials, model algorithms, and model design, which encompasses the design goals, architecture (a three-layered architecture), components, and characteristics of the Hybrid-AutoML toolkit developed in this research for automatic mode and model selection on single or multi-varying datasets. The mode components, decision learning and AutoProbClass unsupervised algorithms, and application API are described. The testing and evaluation of the model is conducted by two case studies.
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