Improving machine learning methods for solving non-stationary conditions based on data availability, time urgency, and types of change

Supervised learning algorithms do not work well when the deployment condition is dissimilar to the training condition. Such non-stationary conditions include covariate shifts and concept shifts. Importance weighted learning (IWL) is used to handle a one-time covariate shift but not frequent shifts a...

全面介绍

书目详细资料
主要作者: Goh, Chun Fan
其他作者: Seet Gim Lee, Gerald
格式: Thesis-Doctor of Philosophy
语言:English
出版: Nanyang Technological University 2021
主题:
在线阅读:https://hdl.handle.net/10356/147041