DocumentCode :
2581238
Title :
A unified learning paradigm for large-scale personalized information management
Author :
Chang, Edward Y. ; Hop, S.C.H. ; Wang, Xingjing ; Wei-Ying Max ; Lyu, Michael R.
Author_Institution :
Electr. & Comput. Eng., California Univ., Santa Barbara, CA, USA
fYear :
2005
fDate :
15-16 Aug. 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.
Keywords :
information management; learning (artificial intelligence); large-scale personalized information management; statistical-learning approach; unified learning paradigm; Clustering algorithms; Convergence; Humans; Information management; Kernel; Large-scale systems; Machine learning; Stability; Supervised learning; Unsupervised learning;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Emerging Information Technology Conference, 2005.
Print_ISBN :
0-7803-9328-7
Type :
conf
DOI :
10.1109/EITC.2005.1544372
Filename :
1544372
Link To Document :
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