随机森林 (Chinese Wikipedia)

Analysis of information sources in references of the Wikipedia article "随机森林" in Chinese language version.

refsWebsite

Global rank Chinese rank

12web.archive.org

1^st place

8doi.org

2^nd place

23^rd place

4nih.gov

4^th place

5^th place

2ieee.org

652^nd place

712^th place

2oxfordjournals.org

1,389^th place

860^th place

2psu.edu

207^th place

628^th place

1bell-labs.com

5,739^th place

2,413^th place

1berkeley.edu

580^th place

642^nd place

1stanford.edu

179^th place

275^th place

1ulg.ac.be

7,875^th place

low place

1r-project.org

7,871^st place

6,207^th place

1researchgate.net

120^th place

337^th place

1uni-muenchen.de

3,337^th place

9,871^st place

1arxiv.org

69^th place

254^th place

1jstor.org

26^th place

113^th place

arxiv.org (Global: 69^th place; Chinese: 254^th place)

Painsky A, Rosset S. Cross-Validated Variable Selection in Tree-Based Methods Improves Predictive Performance. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2017, 39 (11): 2142–2153. PMID 28114007. arXiv:1512.03444 . doi:10.1109/tpami.2016.2636831.

bell-labs.com (Global: 5,739^th place; Chinese: 2,413^th place)

ect.bell-labs.com

Ho, Tin Kam. A Data Complexity Analysis of Comparative Advantages of Decision Forest Constructors (PDF). Pattern Analysis and Applications. 2002: 102–112 [2019-02-16]. （原始内容 (PDF)存档于2016-04-17）.

berkeley.edu (Global: 580^th place; Chinese: 642^nd place)

stat.berkeley.edu

RandomForest2001 (PDF). [2019-07-26]. （原始内容 (PDF)存档于2021-04-03）.

doi.org (Global: 2^nd place; Chinese: 23^rd place)

Tin Kam Ho. Random decision forests. Proceedings of 3rd International Conference on Document Analysis and Recognition (Montreal, Que., Canada: IEEE Comput. Soc. Press). 1995, 1: 278–282 [2020-03-04]. ISBN 978-0-8186-7128-9. doi:10.1109/ICDAR.1995.598994. （原始内容存档于2021-03-03）.
Tin Kam Ho. The random subspace method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence. Aug./1998, 20 (8): 832–844 [2020-03-04]. doi:10.1109/34.709601. （原始内容存档于2021-03-08）. 请检查|date=中的日期值 (帮助)
Geurts P, Ernst D, Wehenkel L. Extremely randomized trees (PDF). Machine Learning. 2006, 63: 3–42 [2019-02-16]. doi:10.1007/s10994-006-6226-1. （原始内容 (PDF)存档于2017-10-31）.
Zhu R, Zeng D, Kosorok MR. Reinforcement Learning Trees. Journal of the American Statistical Association. 2015, 110 (512): 1770–1784. PMC 4760114 . PMID 26903687. doi:10.1080/01621459.2015.1036994.
Altmann A, Toloşi L, Sander O, Lengauer T. Permutation importance: a corrected feature importance measure. Bioinformatics. May 2010, 26 (10): 1340–7 [2019-02-16]. PMID 20385727. doi:10.1093/bioinformatics/btq134. （原始内容存档于2016-11-08）.
Painsky A, Rosset S. Cross-Validated Variable Selection in Tree-Based Methods Improves Predictive Performance. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2017, 39 (11): 2142–2153. PMID 28114007. arXiv:1512.03444 . doi:10.1109/tpami.2016.2636831.
Tolosi L, Lengauer T. Classification with correlated features: unreliability of feature ranking and solutions. Bioinformatics. July 2011, 27 (14): 1986–94 [2019-02-16]. PMID 21576180. doi:10.1093/bioinformatics/btr300. （原始内容存档于2015-08-31）.
Shi, T., Horvath, S. Unsupervised Learning with Random Forest Predictors. Journal of Computational and Graphical Statistics. 2006, 15 (1): 118–138 . CiteSeerX 10.1.1.698.2365 . JSTOR 27594168. doi:10.1198/106186006X94072.

ieee.org (Global: 652^nd place; Chinese: 712^th place)

ieeexplore.ieee.org

Tin Kam Ho. Random decision forests. Proceedings of 3rd International Conference on Document Analysis and Recognition (Montreal, Que., Canada: IEEE Comput. Soc. Press). 1995, 1: 278–282 [2020-03-04]. ISBN 978-0-8186-7128-9. doi:10.1109/ICDAR.1995.598994. （原始内容存档于2021-03-03）.
Tin Kam Ho. The random subspace method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence. Aug./1998, 20 (8): 832–844 [2020-03-04]. doi:10.1109/34.709601. （原始内容存档于2021-03-08）. 请检查|date=中的日期值 (帮助)

jstor.org (Global: 26^th place; Chinese: 113^th place)

Shi, T., Horvath, S. Unsupervised Learning with Random Forest Predictors. Journal of Computational and Graphical Statistics. 2006, 15 (1): 118–138 . CiteSeerX 10.1.1.698.2365 . JSTOR 27594168. doi:10.1198/106186006X94072.

nih.gov (Global: 4^th place; Chinese: 5^th place)

ncbi.nlm.nih.gov

Zhu R, Zeng D, Kosorok MR. Reinforcement Learning Trees. Journal of the American Statistical Association. 2015, 110 (512): 1770–1784. PMC 4760114 . PMID 26903687. doi:10.1080/01621459.2015.1036994.
Altmann A, Toloşi L, Sander O, Lengauer T. Permutation importance: a corrected feature importance measure. Bioinformatics. May 2010, 26 (10): 1340–7 [2019-02-16]. PMID 20385727. doi:10.1093/bioinformatics/btq134. （原始内容存档于2016-11-08）.
Painsky A, Rosset S. Cross-Validated Variable Selection in Tree-Based Methods Improves Predictive Performance. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2017, 39 (11): 2142–2153. PMID 28114007. arXiv:1512.03444 . doi:10.1109/tpami.2016.2636831.
Tolosi L, Lengauer T. Classification with correlated features: unreliability of feature ranking and solutions. Bioinformatics. July 2011, 27 (14): 1986–94 [2019-02-16]. PMID 21576180. doi:10.1093/bioinformatics/btr300. （原始内容存档于2015-08-31）.

oxfordjournals.org (Global: 1,389^th place; Chinese: 860^th place)

bioinformatics.oxfordjournals.org

Altmann A, Toloşi L, Sander O, Lengauer T. Permutation importance: a corrected feature importance measure. Bioinformatics. May 2010, 26 (10): 1340–7 [2019-02-16]. PMID 20385727. doi:10.1093/bioinformatics/btq134. （原始内容存档于2016-11-08）.
Tolosi L, Lengauer T. Classification with correlated features: unreliability of feature ranking and solutions. Bioinformatics. July 2011, 27 (14): 1986–94 [2019-02-16]. PMID 21576180. doi:10.1093/bioinformatics/btr300. （原始内容存档于2015-08-31）.

psu.edu (Global: 207^th place; Chinese: 628^th place)

citeseerx.ist.psu.edu

Lin, Yi; Jeon, Yongho. Random forests and adaptive nearest neighbors (技术报告). Technical Report No. 1055. University of Wisconsin. 2002 [2019-02-16]. （原始内容存档于2013-06-30）.
Shi, T., Horvath, S. Unsupervised Learning with Random Forest Predictors. Journal of Computational and Graphical Statistics. 2006, 15 (1): 118–138 . CiteSeerX 10.1.1.698.2365 . JSTOR 27594168. doi:10.1198/106186006X94072.

r-project.org (Global: 7,871^st place; Chinese: 6,207^th place)

cran.r-project.org

Liaw A. Documentation for R package randomForest (PDF). 16 October 2012 [15 March 2013]. （原始内容 (PDF)存档于2021-03-19）.

researchgate.net (Global: 120^th place; Chinese: 337^th place)

Deng,H.; Runger, G.; Tuv, E. Bias of importance measures for multi-valued attributes and solutions (PDF). Proceedings of the 21st International Conference on Artificial Neural Networks (ICANN): 293–300. 2011 [2019-02-16]. （原始内容 (PDF)存档于2018-08-09）.

stanford.edu (Global: 179^th place; Chinese: 275^th place)

www-stat.stanford.edu

Hastie, Trevor; Tibshirani, Robert; Friedman, Jerome. The Elements of Statistical Learning 2nd. Springer. 2008 [2024-12-07]. ISBN 0-387-95284-5. （原始内容存档于2009-11-10）.

ulg.ac.be (Global: 7,875^th place; Chinese: low place)

orbi.ulg.ac.be

Geurts P, Ernst D, Wehenkel L. Extremely randomized trees (PDF). Machine Learning. 2006, 63: 3–42 [2019-02-16]. doi:10.1007/s10994-006-6226-1. （原始内容 (PDF)存档于2017-10-31）.

uni-muenchen.de (Global: 3,337^th place; Chinese: 9,871^st place)

epub.ub.uni-muenchen.de

Strobl C, Boulesteix AL, Augustin T. Unbiased split selection for classification trees based on the Gini index (PDF). Computational Statistics & Data Analysis. 2007: 483–501 [2019-02-16]. （原始内容 (PDF)存档于2020-11-12）.

web.archive.org (Global: 1^st place; Chinese: 1^st place)

Tin Kam Ho. Random decision forests. Proceedings of 3rd International Conference on Document Analysis and Recognition (Montreal, Que., Canada: IEEE Comput. Soc. Press). 1995, 1: 278–282 [2020-03-04]. ISBN 978-0-8186-7128-9. doi:10.1109/ICDAR.1995.598994. （原始内容存档于2021-03-03）.
Tin Kam Ho. The random subspace method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence. Aug./1998, 20 (8): 832–844 [2020-03-04]. doi:10.1109/34.709601. （原始内容存档于2021-03-08）. 请检查|date=中的日期值 (帮助)
Ho, Tin Kam. A Data Complexity Analysis of Comparative Advantages of Decision Forest Constructors (PDF). Pattern Analysis and Applications. 2002: 102–112 [2019-02-16]. （原始内容 (PDF)存档于2016-04-17）.
RandomForest2001 (PDF). [2019-07-26]. （原始内容 (PDF)存档于2021-04-03）.
Hastie, Trevor; Tibshirani, Robert; Friedman, Jerome. The Elements of Statistical Learning 2nd. Springer. 2008 [2024-12-07]. ISBN 0-387-95284-5. （原始内容存档于2009-11-10）.
Geurts P, Ernst D, Wehenkel L. Extremely randomized trees (PDF). Machine Learning. 2006, 63: 3–42 [2019-02-16]. doi:10.1007/s10994-006-6226-1. （原始内容 (PDF)存档于2017-10-31）.
Liaw A. Documentation for R package randomForest (PDF). 16 October 2012 [15 March 2013]. （原始内容 (PDF)存档于2021-03-19）.
Deng,H.; Runger, G.; Tuv, E. Bias of importance measures for multi-valued attributes and solutions (PDF). Proceedings of the 21st International Conference on Artificial Neural Networks (ICANN): 293–300. 2011 [2019-02-16]. （原始内容 (PDF)存档于2018-08-09）.
Altmann A, Toloşi L, Sander O, Lengauer T. Permutation importance: a corrected feature importance measure. Bioinformatics. May 2010, 26 (10): 1340–7 [2019-02-16]. PMID 20385727. doi:10.1093/bioinformatics/btq134. （原始内容存档于2016-11-08）.
Strobl C, Boulesteix AL, Augustin T. Unbiased split selection for classification trees based on the Gini index (PDF). Computational Statistics & Data Analysis. 2007: 483–501 [2019-02-16]. （原始内容 (PDF)存档于2020-11-12）.
Tolosi L, Lengauer T. Classification with correlated features: unreliability of feature ranking and solutions. Bioinformatics. July 2011, 27 (14): 1986–94 [2019-02-16]. PMID 21576180. doi:10.1093/bioinformatics/btr300. （原始内容存档于2015-08-31）.
Lin, Yi; Jeon, Yongho. Random forests and adaptive nearest neighbors (技术报告). Technical Report No. 1055. University of Wisconsin. 2002 [2019-02-16]. （原始内容存档于2013-06-30）.

随机森林 (Chinese Wikipedia)

arxiv.org (Global: 69th place; Chinese: 254th place)

bell-labs.com (Global: 5,739th place; Chinese: 2,413th place)

ect.bell-labs.com

berkeley.edu (Global: 580th place; Chinese: 642nd place)

stat.berkeley.edu

doi.org (Global: 2nd place; Chinese: 23rd place)

ieee.org (Global: 652nd place; Chinese: 712th place)

ieeexplore.ieee.org

jstor.org (Global: 26th place; Chinese: 113th place)

nih.gov (Global: 4th place; Chinese: 5th place)