Las losowy (Polish Wikipedia)

Analysis of information sources in references of the Wikipedia article "Las losowy" in Polish language version.

refsWebsite

Global rank Polish rank

16doi.org

2^nd place

6^th place

6web.archive.org

1^st place

4nih.gov

4^th place

7^th place

3bell-labs.com

5,739^th place

6,374^th place

2semanticscholar.org

11^th place

1,348^th place

2archive.ph

756^th place

32^nd place

2oxfordjournals.org

1,389^th place

2,042^nd place

1projecteuclid.org

3,707^th place

4,048^th place

1ams.org

451^st place

5,248^th place

1r-project.org

7,871^st place

low place

1justia.com

696^th place

7,282^nd place

1jhu.edu

699^th place

2,079^th place

1usc.edu

1,077^th place

2,015^th place

1ulg.ac.be

7,875^th place

low place

1researchgate.net

120^th place

180^th place

1uni-muenchen.de

3,337^th place

low place

1arxiv.org

69^th place

224^th place

ams.org

E.M.E.M. Kleinberg E.M.E.M., An overtraining-resistant stochastic modeling method for pattern recognition, „Ann. Statist.”, 24 (6), 1996, s. 2319–2349, DOI: 10.1214/aos/1032181157, MR 1425956 .

archive.ph

AndréA. Altmann AndréA. i inni, Permutation importance: a corrected feature importance measure, „Bioinformatics”, 26 (10), 2010, s. 1340–1347, DOI: 10.1093/bioinformatics/btq134, PMID: 20385727 [zarchiwizowane z adresu 2013-04-15] .
LauraL. Toloşi LauraL., ThomasT. Lengauer ThomasT., Classification with correlated features: unreliability of feature ranking and solutions, „Bioinformatics”, 27 (14), 2011, s. 1986–1994, DOI: 10.1093/bioinformatics/btr300, PMID: 21576180 [zarchiwizowane z adresu 2013-04-15] .

arxiv.org

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

bell-labs.com

ect.bell-labs.com

Wayback Machine [online], ect.bell-labs.com [dostęp 2019-11-25] [zarchiwizowane z adresu 2016-04-17] .
Tin KamT.K. Ho Tin KamT.K., The random subspace method for constructing decision forests, „IEEE Transactions on Pattern Analysis and Machine Intelligence”, 20 (8), 1998, s. 832–844, DOI: 10.1109/34.709601 [dostęp 2019-05-25] [zarchiwizowane z adresu 2016-03-04] .
Tin KamT.K. Ho Tin KamT.K., A Data Complexity Analysis of Comparative Advantages of Decision Forest Constructors, „Pattern Analysis & Applications”, 5 (2), 2002, s. 102–112, DOI: 10.1007/s100440200009 [dostęp 2019-05-25] [zarchiwizowane z adresu 2016-04-17] .

doi.org

dx.doi.org

Tin KamT.K. Ho Tin KamT.K., The random subspace method for constructing decision forests, „IEEE Transactions on Pattern Analysis and Machine Intelligence”, 20 (8), 1998, s. 832–844, DOI: 10.1109/34.709601 [dostęp 2019-05-25] [zarchiwizowane z adresu 2016-03-04] .
E.M.E.M. Kleinberg E.M.E.M., Stochastic discrimination, „Annals of Mathematics and Artificial Intelligence”, 1 (1–4), 1990, s. 207–239, DOI: 10.1007/BF01531079 [dostęp 2019-05-25] [zarchiwizowane z adresu 2018-01-18] .
E.M.E.M. Kleinberg E.M.E.M., An overtraining-resistant stochastic modeling method for pattern recognition, „Ann. Statist.”, 24 (6), 1996, s. 2319–2349, DOI: 10.1214/aos/1032181157, MR 1425956 .
E.M.E.M. Kleinberg E.M.E.M., On the algorithmic implementation of stochastic discrimination, „IEEE Transactions on Pattern Analysis and Machine Intelligence”, 22 (5), 2000, s. 473–490, DOI: 10.1109/34.857004 [dostęp 2019-05-25] [zarchiwizowane z adresu 2018-01-18] .
LeoL. Breiman LeoL., Random Forests, „Machine Learning”, 45 (1), 2001, s. 5–32, DOI: 10.1023/A:1010933404324 .
YaliY. Amit YaliY., DonaldD. Geman DonaldD., Shape Quantization and Recognition with Randomized Trees, „Neural Computation”, 9 (7), 1997, s. 1545–1588, DOI: 10.1162/neco.1997.9.7.1545 [dostęp 2019-05-25] [zarchiwizowane z adresu 2018-02-05] .
Tin KamT.K. Ho Tin KamT.K., A Data Complexity Analysis of Comparative Advantages of Decision Forest Constructors, „Pattern Analysis & Applications”, 5 (2), 2002, s. 102–112, DOI: 10.1007/s100440200009 [dostęp 2019-05-25] [zarchiwizowane z adresu 2016-04-17] .
PierreP. Geurts PierreP., DamienD. Ernst DamienD., LouisL. Wehenkel LouisL., Extremely randomized trees, „Machine Learning”, 63 (1), 2006, s. 3–42, DOI: 10.1007/s10994-006-6226-1 .
RuoqingR. Zhu RuoqingR., DonglinD. Zeng DonglinD., Michael R.M.R. Kosorok Michael R.M.R., Reinforcement Learning Trees, „Journal of the American Statistical Association”, 110 (512), 2015, s. 1770–1784, DOI: 10.1080/01621459.2015.1036994, PMID: 26903687, PMCID: PMC4760114 .
AndréA. Altmann AndréA. i inni, Permutation importance: a corrected feature importance measure, „Bioinformatics”, 26 (10), 2010, s. 1340–1347, DOI: 10.1093/bioinformatics/btq134, PMID: 20385727 [zarchiwizowane z adresu 2013-04-15] .
CarolinC. Strobl CarolinC., Anne-LaureA.L. Boulesteix Anne-LaureA.L., ThomasT. Augustin ThomasT., Unbiased split selection for classification trees based on the Gini Index, „Computational Statistics & Data Analysis”, 52 (1), 2007, s. 483–501, DOI: 10.1016/j.csda.2006.12.030 .
AmichaiA. Painsky AmichaiA., SaharonS. Rosset SaharonS., Cross-Validated Variable Selection in Tree-Based Methods Improves Predictive Performance, „IEEE Transactions on Pattern Analysis and Machine Intelligence”, 39 (11), 2017, s. 2142–2153, DOI: 10.1109/tpami.2016.2636831, PMID: 28114007, arXiv:1512.03444 .
LauraL. Toloşi LauraL., ThomasT. Lengauer ThomasT., Classification with correlated features: unreliability of feature ranking and solutions, „Bioinformatics”, 27 (14), 2011, s. 1986–1994, DOI: 10.1093/bioinformatics/btr300, PMID: 21576180 [zarchiwizowane z adresu 2013-04-15] .
KrzysztofK. Węcel KrzysztofK., WłodzimierzW. Lewoniewski WłodzimierzW., Modelling the Quality of Attributes in Wikipedia Infoboxes, BIS 2015: Business Information Systems Workshops, 2015, s. 308–320, DOI: 10.1007/978-3-319-26762-3_27, ISBN 978-3-319-26761-6 .
WłodzimierzW. Lewoniewski WłodzimierzW., KrzysztofK. Węcel KrzysztofK., WitoldW. Abramowicz WitoldW., Quality and Importance of Wikipedia Articles in Different Languages, ICIST 2016: Information and Software Technologies, 2016, s. 613–624, DOI: 10.1007/978-3-319-46254-7_50, ISBN 978-3-319-46253-0 .
MortenM. Warncke-Wang MortenM., DanD. Cosley DanD., JohnJ. Riedl JohnJ., Tell me more: an actionable quality model for Wikipedia, Hong Kong, China: WikiSym '13 Proceedings of the 9th International Symposium on Open Collaboration, 5 sierpnia 2013, DOI: 10.1145/2491055.2491063, ISBN 978-1-4503-1852-5, Article No. 8 .

jhu.edu

cis.jhu.edu

YaliY. Amit YaliY., DonaldD. Geman DonaldD., Shape Quantization and Recognition with Randomized Trees, „Neural Computation”, 9 (7), 1997, s. 1545–1588, DOI: 10.1162/neco.1997.9.7.1545 [dostęp 2019-05-25] [zarchiwizowane z adresu 2018-02-05] .

justia.com

trademarks.justia.com

RANDOM FORESTS Trademark of Health Care Productivity, Inc. – Registration Number 3185828 – Serial Number 78642027 :: Justia Trademarks.

nih.gov

ncbi.nlm.nih.gov

RuoqingR. Zhu RuoqingR., DonglinD. Zeng DonglinD., Michael R.M.R. Kosorok Michael R.M.R., Reinforcement Learning Trees, „Journal of the American Statistical Association”, 110 (512), 2015, s. 1770–1784, DOI: 10.1080/01621459.2015.1036994, PMID: 26903687, PMCID: PMC4760114 .
AndréA. Altmann AndréA. i inni, Permutation importance: a corrected feature importance measure, „Bioinformatics”, 26 (10), 2010, s. 1340–1347, DOI: 10.1093/bioinformatics/btq134, PMID: 20385727 [zarchiwizowane z adresu 2013-04-15] .
AmichaiA. Painsky AmichaiA., SaharonS. Rosset SaharonS., Cross-Validated Variable Selection in Tree-Based Methods Improves Predictive Performance, „IEEE Transactions on Pattern Analysis and Machine Intelligence”, 39 (11), 2017, s. 2142–2153, DOI: 10.1109/tpami.2016.2636831, PMID: 28114007, arXiv:1512.03444 .
LauraL. Toloşi LauraL., ThomasT. Lengauer ThomasT., Classification with correlated features: unreliability of feature ranking and solutions, „Bioinformatics”, 27 (14), 2011, s. 1986–1994, DOI: 10.1093/bioinformatics/btr300, PMID: 21576180 [zarchiwizowane z adresu 2013-04-15] .

oxfordjournals.org

bioinformatics.oxfordjournals.org

AndréA. Altmann AndréA. i inni, Permutation importance: a corrected feature importance measure, „Bioinformatics”, 26 (10), 2010, s. 1340–1347, DOI: 10.1093/bioinformatics/btq134, PMID: 20385727 [zarchiwizowane z adresu 2013-04-15] .
LauraL. Toloşi LauraL., ThomasT. Lengauer ThomasT., Classification with correlated features: unreliability of feature ranking and solutions, „Bioinformatics”, 27 (14), 2011, s. 1986–1994, DOI: 10.1093/bioinformatics/btr300, PMID: 21576180 [zarchiwizowane z adresu 2013-04-15] .

projecteuclid.org

E.M.E.M. Kleinberg E.M.E.M., An overtraining-resistant stochastic modeling method for pattern recognition, „Ann. Statist.”, 24 (6), 1996, s. 2319–2349, DOI: 10.1214/aos/1032181157, MR 1425956 .

r-project.org

cran.r-project.org

randomForest.pdf.

researchgate.net

H.H. Deng H.H., G.G. Runger G.G., E.E. Tuv E.E., Bias of importance measures for multi-valued attributes and solutions, „Proceedings of the 21st International Conference on Artificial Neural Networks (ICANN)”, 2011, s. 293–300 .

semanticscholar.org

pdfs.semanticscholar.org

E.M.E.M. Kleinberg E.M.E.M., Stochastic discrimination, „Annals of Mathematics and Artificial Intelligence”, 1 (1–4), 1990, s. 207–239, DOI: 10.1007/BF01531079 [dostęp 2019-05-25] [zarchiwizowane z adresu 2018-01-18] .
E.M.E.M. Kleinberg E.M.E.M., On the algorithmic implementation of stochastic discrimination, „IEEE Transactions on Pattern Analysis and Machine Intelligence”, 22 (5), 2000, s. 473–490, DOI: 10.1109/34.857004 [dostęp 2019-05-25] [zarchiwizowane z adresu 2018-01-18] .

ulg.ac.be

orbi.ulg.ac.be

PierreP. Geurts PierreP., DamienD. Ernst DamienD., LouisL. Wehenkel LouisL., Extremely randomized trees, „Machine Learning”, 63 (1), 2006, s. 3–42, DOI: 10.1007/s10994-006-6226-1 .

uni-muenchen.de

epub.ub.uni-muenchen.de

CarolinC. Strobl CarolinC., Anne-LaureA.L. Boulesteix Anne-LaureA.L., ThomasT. Augustin ThomasT., Unbiased split selection for classification trees based on the Gini Index, „Computational Statistics & Data Analysis”, 52 (1), 2007, s. 483–501, DOI: 10.1016/j.csda.2006.12.030 .

usc.edu

www-bcf.usc.edu

Introduction to Statistical Learning.

web.archive.org

Wayback Machine [online], ect.bell-labs.com [dostęp 2019-11-25] [zarchiwizowane z adresu 2016-04-17] .
Tin KamT.K. Ho Tin KamT.K., The random subspace method for constructing decision forests, „IEEE Transactions on Pattern Analysis and Machine Intelligence”, 20 (8), 1998, s. 832–844, DOI: 10.1109/34.709601 [dostęp 2019-05-25] [zarchiwizowane z adresu 2016-03-04] .
E.M.E.M. Kleinberg E.M.E.M., Stochastic discrimination, „Annals of Mathematics and Artificial Intelligence”, 1 (1–4), 1990, s. 207–239, DOI: 10.1007/BF01531079 [dostęp 2019-05-25] [zarchiwizowane z adresu 2018-01-18] .
E.M.E.M. Kleinberg E.M.E.M., On the algorithmic implementation of stochastic discrimination, „IEEE Transactions on Pattern Analysis and Machine Intelligence”, 22 (5), 2000, s. 473–490, DOI: 10.1109/34.857004 [dostęp 2019-05-25] [zarchiwizowane z adresu 2018-01-18] .
YaliY. Amit YaliY., DonaldD. Geman DonaldD., Shape Quantization and Recognition with Randomized Trees, „Neural Computation”, 9 (7), 1997, s. 1545–1588, DOI: 10.1162/neco.1997.9.7.1545 [dostęp 2019-05-25] [zarchiwizowane z adresu 2018-02-05] .
Tin KamT.K. Ho Tin KamT.K., A Data Complexity Analysis of Comparative Advantages of Decision Forest Constructors, „Pattern Analysis & Applications”, 5 (2), 2002, s. 102–112, DOI: 10.1007/s100440200009 [dostęp 2019-05-25] [zarchiwizowane z adresu 2016-04-17] .