TY  - CONF
AU  - LIU, Tie-Yan
AU  - YANG, Yiming
AU  - WAN, Hao
AU  - ZHOU, Qian
AU  - GAO, Bin
AU  - ZENG, Hua-Jun
AU  - CHEN, Zheng
AU  - MA, Wei-Ying
A2  - 
T1  - An experimental study on large-scale web categorization
T2  - Special interest tracks and posters of the 14th international conference on World Wide Web
PB  - ACM
CY  - New York, NY, USA
PY  - 2005/
M2  - 
VL  - 
IS  - 
SP  - 1106
EP  - 1107
UR  - http://doi.acm.org/10.1145/1062745.1062891
M3  - 10.1145/1062745.1062891
KW  - categorization
KW  - bachelor:2011:bachmann
KW  - web
L1  - 
SN  - 1-59593-051-5
N1  - An experimental study on large-scale web categorization
N1  - 
AB  - Taxonomies of the Web typically have hundreds of thousands of categories and skewed category distribution over documents. It is not clear whether existing text classification technologies can perform well on and scale up to such large-scale applications. To understand this, we conducted the evaluation of several representative methods (Support Vector Machines, <i>k</i>-Nearest Neighbor and Naive Bayes) with Yahoo! taxonomies. In particular, we evaluated the effectiveness/efficiency tradeoff in classifiers with hierarchical setting compared to conventional (flat) setting, and tested popular threshold tuning strategies for their scalability and accuracy in large-scale classification problems.
ER  -

TY  - CONF
AU  - Kubica, Jeremy
AU  - Moore, Andrew
AU  - Schneider, Jeff
AU  - Yang, Yiming
A2  - 
T1  - Stochastic Link and Group Detection
T2  - Proceedings of the Eighteenth National Conference on Artificial Intelligence
PB  - AAAI Press/MIT Press
CY  - 
PY  - 2002/07
M2  - 
VL  - 
IS  - 
SP  - 798
EP  - 804
UR  - 
M3  - 
KW  - detection
KW  - gda
KW  - community
KW  - network
L1  - 
SN  - 
N1  - 
N1  - 
AB  - 
ER  -