{"title":"线性回归","authors":"David J. Olive","doi":"10.1002/9781119404729.ch13","DOIUrl":null,"url":null,"abstract":"Much of machine learning is about fitting functions to data. That may not sound like an exciting activity that will give us artificial intelligence. However, representing and fitting functions is a core building block of most working machine learning or AI systems. We start with linear functions, both because this idea turns out to be surprisingly powerful, and because it’s a useful starting point for more interesting models and methods.","PeriodicalId":296341,"journal":{"name":"Machine Learning and Big Data with kdb+/q","volume":"28 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Linear Regression\",\"authors\":\"David J. Olive\",\"doi\":\"10.1002/9781119404729.ch13\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Much of machine learning is about fitting functions to data. That may not sound like an exciting activity that will give us artificial intelligence. However, representing and fitting functions is a core building block of most working machine learning or AI systems. We start with linear functions, both because this idea turns out to be surprisingly powerful, and because it’s a useful starting point for more interesting models and methods.\",\"PeriodicalId\":296341,\"journal\":{\"name\":\"Machine Learning and Big Data with kdb+/q\",\"volume\":\"28 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-05-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Machine Learning and Big Data with kdb+/q\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1002/9781119404729.ch13\",\"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 and Big Data with kdb+/q","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1002/9781119404729.ch13","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Much of machine learning is about fitting functions to data. That may not sound like an exciting activity that will give us artificial intelligence. However, representing and fitting functions is a core building block of most working machine learning or AI systems. We start with linear functions, both because this idea turns out to be surprisingly powerful, and because it’s a useful starting point for more interesting models and methods.
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