Transfer learning with high dimensional composite quantile regression

IF 1.1 4区数学 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Statistical Computation and Simulation Pub Date : 2024-03-20 DOI:10.1080/00949655.2024.2329969

Jie Li, Yunquan Song

引用次数: 0

Abstract

Transfer learning has gained wide popularity due to its ability to enhance the performance of target tasks by utilizing external information sources. Despite this, current transfer learning methods...

查看原文本刊更多论文

利用高维复合量化回归进行迁移学习

迁移学习能够通过利用外部信息源来提高目标任务的绩效，因此广受欢迎。尽管如此，目前的迁移学习方法...

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Journal of Statistical Computation and Simulation 数学-计算机：跨学科应用

CiteScore

2.30

自引率

8.30%

发文量

156

审稿时长

4-8 weeks

期刊介绍： Journal of Statistical Computation and Simulation ( JSCS ) publishes significant and original work in areas of statistics which are related to or dependent upon the computer. Fields covered include computer algorithms related to probability or statistics, studies in statistical inference by means of simulation techniques, and implementation of interactive statistical systems. JSCS does not consider applications of statistics to other fields, except as illustrations of the use of the original statistics presented. Accepted papers should ideally appeal to a wide audience of statisticians and provoke real applications of theoretical constructions.