A unified learning paradigm for large-scale personalized information management
Publication Type
Conference Proceeding Article
Publication Date
8-2005
Abstract
Statistical-learning approaches such as unsupervised learning, supervised learning, active learning, and reinforcement learning have generally been separately studied and applied to solve application problems. In this paper, we provide an overview of our newly proposed unified learning paradigm (ULP), which combines these approaches into one synergistic framework. We outline the architecture and the algorithm of ULP, and explain benefits of employing this unified learning paradigm on personalizing information management.
Discipline
Databases and Information Systems
Research Areas
Data Science and Engineering
Publication
Emerging Information Technology Conference EITC 2005: Taipei, Taiwan, 15-16 August
Volume
2005
First Page
151
Last Page
154
ISBN
9780780393295
Identifier
10.1109/EITC.2005.1544372
Publisher
IEEE
City or Country
Piscataway, NJ
Citation
CHANG, Edward Y.; HOI, Steven C. H.; WANG, Xinjing; MA, Wei-Ying; and LYU, Michael R..
A unified learning paradigm for large-scale personalized information management. (2005). Emerging Information Technology Conference EITC 2005: Taipei, Taiwan, 15-16 August. 2005, 151-154.
Available at: https://ink.library.smu.edu.sg/sis_research/4199
Additional URL
https://doi.org/10.1109/EITC.2005.1544372