An experimental study on large-scale web categorization
T. LIU, Y. YANG, H. WAN, Q. ZHOU, B. GAO, H. ZENG, Z. CHEN, и W. MA.
Special interest tracks and posters of the 14th international conference on World Wide Web, стр. 1106--1107. New York, NY, USA, ACM, (2005)

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.

URL

http://doi.acm.org/10.1145/1062745.1062891

искать в

К этой публикации ещё не было создано рецензий.

распределение оценок

средняя оценка пользователей0,0 из 5.0 на основе 0 рецензий

Пожалуйста, войдите в систему, чтобы принять участие в дискуссии (добавить собственные рецензию, или комментарий)

@inproceedings{liu2005experimental,
  abstract = {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.},
  acmid = {1062891},
  added-at = {2011-11-21T10:54:31.000+0100},
  address = {New York, NY, USA},
  author = {LIU, Tie-Yan and YANG, Yiming and WAN, Hao and ZHOU, Qian and GAO, Bin and ZENG, Hua-Jun and CHEN, Zheng and MA, Wei-Ying},
  biburl = {https://puma.uni-kassel.de/bibtex/236cc9f92b9c722b2aff441b23e44b2f7/benz},
  booktitle = {Special interest tracks and posters of the 14th international conference on World Wide Web},
  description = {An experimental study on large-scale web categorization},
  doi = {10.1145/1062745.1062891},
  interhash = {e581e4dd2ed6d748031a812c724c4b7c},
  intrahash = {36cc9f92b9c722b2aff441b23e44b2f7},
  isbn = {1-59593-051-5},
  keywords = {categorization web bachelor:2011:bachmann},
  location = {Chiba, Japan},
  numpages = {2},
  pages = {1106--1107},
  publisher = {ACM},
  series = {WWW '05},
  timestamp = {2011-11-21T11:04:04.000+0100},
  title = {An experimental study on large-scale web categorization},
  url = {http://doi.acm.org/10.1145/1062745.1062891},
  year = 2005
}

%0 Conference Paper
%1 liu2005experimental
%A LIU, Tie-Yan
%A YANG, Yiming
%A WAN, Hao
%A ZHOU, Qian
%A GAO, Bin
%A ZENG, Hua-Jun
%A CHEN, Zheng
%A MA, Wei-Ying
%B Special interest tracks and posters of the 14th international conference on World Wide Web
%C New York, NY, USA
%D 2005
%I ACM
%K categorization web bachelor:2011:bachmann
%P 1106--1107
%R 10.1145/1062745.1062891
%T An experimental study on large-scale web categorization
%U http://doi.acm.org/10.1145/1062745.1062891
%X 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.
%@ 1-59593-051-5

PUMA

An experimental study on large-scale web categorization
T. LIU, Y. YANG, H. WAN, Q. ZHOU, B. GAO, H. ZENG, Z. CHEN, и W. MA.
Special interest tracks and posters of the 14th international conference on World Wide Web, стр. 1106--1107. New York, NY, USA, ACM, (2005)

Tags

Пользователи данного ресурса

Комментарии и рецензии

Цитировать эту публикацию

PUMA

An experimental study on large-scale web categorizationT. LIU, Y. YANG, H. WAN, Q. ZHOU, B. GAO, H. ZENG, Z. CHEN, и W. MA. Special interest tracks and posters of the 14th international conference on World Wide Web, стр. 1106--1107. New York, NY, USA, ACM, (2005)

Tags

Пользователи данного ресурса

Комментарии и рецензии

Цитировать эту публикацию

An experimental study on large-scale web categorization
T. LIU, Y. YANG, H. WAN, Q. ZHOU, B. GAO, H. ZENG, Z. CHEN, и W. MA.
Special interest tracks and posters of the 14th international conference on World Wide Web, стр. 1106--1107. New York, NY, USA, ACM, (2005)