{"title":"聚类与预测模型构建","authors":"Y. Kistenev, A. Borisov, D. Vrazhnov","doi":"10.1117/3.2599935.CH4","DOIUrl":null,"url":null,"abstract":"The most crucial step in the machine learning pipeline is related to experimental data content and semantic analysis to predict new data’s meaning. The methods and algorithms of supervised and supervised learning are presented in this chapter. The Python codes for the most useful analytical methods described in the chapter are presented in the Supplemental Materials.","PeriodicalId":285501,"journal":{"name":"Medical Applications of Laser Molecular Imaging and Machine Learning","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Clusterization and Predictive Model Construction\",\"authors\":\"Y. Kistenev, A. Borisov, D. Vrazhnov\",\"doi\":\"10.1117/3.2599935.CH4\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The most crucial step in the machine learning pipeline is related to experimental data content and semantic analysis to predict new data’s meaning. The methods and algorithms of supervised and supervised learning are presented in this chapter. The Python codes for the most useful analytical methods described in the chapter are presented in the Supplemental Materials.\",\"PeriodicalId\":285501,\"journal\":{\"name\":\"Medical Applications of Laser Molecular Imaging and Machine Learning\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-07-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Medical Applications of Laser Molecular Imaging and Machine Learning\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1117/3.2599935.CH4\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Medical Applications of Laser Molecular Imaging and Machine Learning","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1117/3.2599935.CH4","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The most crucial step in the machine learning pipeline is related to experimental data content and semantic analysis to predict new data’s meaning. The methods and algorithms of supervised and supervised learning are presented in this chapter. The Python codes for the most useful analytical methods described in the chapter are presented in the Supplemental Materials.
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