A unified learning paradigm for large-scale personalized information management

Edward Y. Chang; Steven C. H. Hoi; Xinjing Wang; Ma. Wei-Ying; Michael R. Lyu

doi:10.1109/EITC.2005.1544372

A unified learning paradigm for large-scale personalized information management

Edward Y. Chang
, Steven C. H. Hoi
, Xinjing Wang
, Ma. Wei-Ying
, Michael R. Lyu

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

3 Citations (Scopus)

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. © 2005 IEEE.

Original language	English
Title of host publication	Emerging Information Technology Conference 2005
Pages	151-154
Volume	2005
DOIs	https://doi.org/10.1109/EITC.2005.1544372
Publication status	Published - 2005
Externally published	Yes
Event	Emerging Information Technology Conference 2005 - Taipei, Taiwan, China Duration: 15 Aug 2005 → 16 Aug 2005

Publication series

Name	Emerging Information Technology Conference 2005
Volume	2005

Conference

Conference	Emerging Information Technology Conference 2005
Place	Taiwan, China
City	Taipei
Period	15/08/05 → 16/08/05

Bibliographical note

Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

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@inproceedings{d367a3c0ee584c2997cd71d56ce0fc34,

title = "A unified learning paradigm for large-scale personalized information management",

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. {\textcopyright} 2005 IEEE.",

author = "Chang, \{Edward Y.\} and Hoi, \{Steven C. H.\} and Xinjing Wang and Ma. Wei-Ying and Lyu, \{Michael R.\}",

note = "Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].; Emerging Information Technology Conference 2005 ; Conference date: 15-08-2005 Through 16-08-2005",

year = "2005",

doi = "10.1109/EITC.2005.1544372",

language = "English",

isbn = "0780393295",

volume = "2005",

series = "Emerging Information Technology Conference 2005",

pages = "151--154",

booktitle = "Emerging Information Technology Conference 2005",

}

Chang, EY, Hoi, SCH, Wang, X, Wei-Ying, M & Lyu, MR 2005, A unified learning paradigm for large-scale personalized information management. in Emerging Information Technology Conference 2005. vol. 2005, 1544372, Emerging Information Technology Conference 2005, vol. 2005, pp. 151-154, Emerging Information Technology Conference 2005, Taipei, Taiwan, China, 15/08/05. https://doi.org/10.1109/EITC.2005.1544372

A unified learning paradigm for large-scale personalized information management. / Chang, Edward Y.; Hoi, Steven C. H.; Wang, Xinjing et al.
Emerging Information Technology Conference 2005. Vol. 2005 2005. p. 151-154 1544372 (Emerging Information Technology Conference 2005; Vol. 2005).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - A unified learning paradigm for large-scale personalized information management

AU - Chang, Edward Y.

AU - Hoi, Steven C. H.

AU - Wang, Xinjing

AU - Wei-Ying, Ma.

AU - Lyu, Michael R.

N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

PY - 2005

Y1 - 2005

N2 - 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. © 2005 IEEE.

AB - 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. © 2005 IEEE.

UR - https://www.scopus.com/pages/publications/33751191408

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-33751191408&origin=recordpage

U2 - 10.1109/EITC.2005.1544372

DO - 10.1109/EITC.2005.1544372

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 0780393295

SN - 9780780393295

VL - 2005

T3 - Emerging Information Technology Conference 2005

SP - 151

EP - 154

BT - Emerging Information Technology Conference 2005

T2 - Emerging Information Technology Conference 2005

Y2 - 15 August 2005 through 16 August 2005

ER -

A unified learning paradigm for large-scale personalized information management

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