{"title":"一个简单的例子，交错学习使用卡尔曼滤波线性最小二乘","authors":"Majnu John , Yihren Wu","doi":"10.1016/j.rinam.2023.100409","DOIUrl":null,"url":null,"abstract":"<div><p>Interleaved learning in machine learning algorithms is a biologically inspired training method with promising results. In this short note, we illustrate the interleaving mechanism via a simple statistical and optimization framework based on Kalman Filter for Linear Least Squares.</p></div>","PeriodicalId":36918,"journal":{"name":"Results in Applied Mathematics","volume":"20 ","pages":"Article 100409"},"PeriodicalIF":1.4000,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2590037423000559/pdfft?md5=221d8de6c07f83bd631a4771a4be4c73&pid=1-s2.0-S2590037423000559-main.pdf","citationCount":"0","resultStr":"{\"title\":\"A simple illustration of interleaved learning using Kalman filter for linear least squares\",\"authors\":\"Majnu John , Yihren Wu\",\"doi\":\"10.1016/j.rinam.2023.100409\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Interleaved learning in machine learning algorithms is a biologically inspired training method with promising results. In this short note, we illustrate the interleaving mechanism via a simple statistical and optimization framework based on Kalman Filter for Linear Least Squares.</p></div>\",\"PeriodicalId\":36918,\"journal\":{\"name\":\"Results in Applied Mathematics\",\"volume\":\"20 \",\"pages\":\"Article 100409\"},\"PeriodicalIF\":1.4000,\"publicationDate\":\"2023-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2590037423000559/pdfft?md5=221d8de6c07f83bd631a4771a4be4c73&pid=1-s2.0-S2590037423000559-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Results in Applied Mathematics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2590037423000559\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MATHEMATICS, APPLIED\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Results in Applied Mathematics","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2590037423000559","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATHEMATICS, APPLIED","Score":null,"Total":0}
A simple illustration of interleaved learning using Kalman filter for linear least squares
Interleaved learning in machine learning algorithms is a biologically inspired training method with promising results. In this short note, we illustrate the interleaving mechanism via a simple statistical and optimization framework based on Kalman Filter for Linear Least Squares.
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