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Wai Lam
Publications
- 2013
- [i2]Wai Lam, Fahiem Bacchus:
Using New Data to Refine a Bayesian Network. CoRR abs/1302.6826 (2013) - [i1]Wai Lam, Fahiem Bacchus:
Using Causal Information and Local Measures to Learn Bayesian Networks. CoRR abs/1303.1483 (2013) - 2007
- [c68]Ka Kan Lo, Wai Lam:
Building Knowledge Base for Reading from Encyclopedia. AAAI Spring Symposium: Machine Reading 2007: 73-78 - 2006
- [c59]Ka Kan Lo, Wai Lam:
Using Semantic Relations with World Knowledge for Question Answering. TREC 2006 - 2005
- [c57]Hon-Man Ko, Wai Lam:
A New Approach for Semi-supervised Online News Classification. Human.Society@Internet 2005: 238-247 - 2003
- [j21]Wai Lam, Kwok Leung Yu:
High-Dimensional Learning Framework for Adaptive Document Filtering. Comput. Intell. 19(1): 42-63 (2003) - 2001
- [c19]Kwok-Yin Lai, Wai Lam:
Meta-learning Models for Automatic Textual Document Categorization. PAKDD 2001: 78-89 - [c18]Kwok-Yin Lai, Wai Lam:
Automatic Textual Document Categorization Using Multiple Similarity-Based Models. SDM 2001: 1-16 - [c17]Wai Lam, Kwok-Yin Lai:
A Meta-Learning Approach for Text Categorization. SIGIR 2001: 303-309 - 2000
- [c16]Wai Lam, Wai-Yip Lin:
Learning to Extract Hierarchical Information from Semi-structured Documents. CIKM 2000: 250-257 - 1999
- [c14]King-Kwok Ng, Wai Lam:
Updating of Association Rules Dynamically. DANTE 1999: 84- - [c12]Wai Lam, Kwok Leung Yu:
An Intelligent Adaptive Filtering Agent Based on an On-Line Learning Model (poster abstract). SIGIR 1999: 287-288 - 1998
- [c11]Kwok Leung Yu, Wai Lam:
A New On-Line Learning Algorithm for Adaptive Text Filtering. CIKM 1998: 156-160 - 1994
- [j1]Wai Lam, Fahiem Bacchus:
Learning Bayesian Belief Networks: An Approach Based on the MDL Principle. Comput. Intell. 10: 269-294 (1994) - [c2]Wai Lam, Fahiem Bacchus:
Using New Data to Refine a Bayesian Network. UAI 1994: 383-390 - 1993
- [c1]Wai Lam, Fahiem Bacchus:
Using Causal Information and Local Measures to Learn Bayesian Networks. UAI 1993: 243-250
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last updated on 2024-04-14 03:43 CEST by the dblp team
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