Naive Bayes classifier (English Wikipedia)

Analysis of information sources in references of the Wikipedia article "Naive Bayes classifier" in English language version.

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5ghostarchive.org

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acm.org

dl.acm.org

John, George H.; Langley, Pat (1995). Estimating Continuous Distributions in Bayesian Classifiers. Proc. Eleventh Conf. on Uncertainty in Artificial Intelligence. Morgan Kaufmann. pp. 338–345. arXiv:1302.4964.

arxiv.org

John, George H.; Langley, Pat (1995). Estimating Continuous Distributions in Bayesian Classifiers. Proc. Eleventh Conf. on Uncertainty in Artificial Intelligence. Morgan Kaufmann. pp. 338–345. arXiv:1302.4964.

doi.org

Hand, D. J.; Yu, K. (2001). "Idiot's Bayes — not so stupid after all?". International Statistical Review. 69 (3): 385–399. doi:10.2307/1403452. ISSN 0306-7734. JSTOR 1403452.
Piryonesi, S. Madeh; El-Diraby, Tamer E. (2020-06-01). "Role of Data Analytics in Infrastructure Asset Management: Overcoming Data Size and Quality Problems". Journal of Transportation Engineering, Part B: Pavements. 146 (2): 04020022. doi:10.1061/JPEODX.0000175. S2CID 216485629.
Nigam, Kamal; McCallum, Andrew; Thrun, Sebastian; Mitchell, Tom (2000). "Learning to classify text from labeled and unlabeled documents using EM" (PDF). Machine Learning. 39 (2/3): 103–134. doi:10.1023/A:1007692713085. S2CID 686980. Archived (PDF) from the original on 2022-10-09.
Niculescu-Mizil, Alexandru; Caruana, Rich (2005). Predicting good probabilities with supervised learning (PDF). ICML. doi:10.1145/1102351.1102430. Archived from the original (PDF) on 2014-03-11. Retrieved 2016-04-24.

ghostarchive.org

McCallum, Andrew. "Graphical Models, Lecture2: Bayesian Network Representation" (PDF). Archived (PDF) from the original on 2022-10-09. Retrieved 22 October 2019.
McCallum, Andrew; Nigam, Kamal (1998). A comparison of event models for Naive Bayes text classification (PDF). AAAI-98 workshop on learning for text categorization. Vol. 752. Archived (PDF) from the original on 2022-10-09.
Rennie, J.; Shih, L.; Teevan, J.; Karger, D. (2003). Tackling the poor assumptions of naive Bayes classifiers (PDF). ICML. Archived (PDF) from the original on 2022-10-09.
Nigam, Kamal; McCallum, Andrew; Thrun, Sebastian; Mitchell, Tom (2000). "Learning to classify text from labeled and unlabeled documents using EM" (PDF). Machine Learning. 39 (2/3): 103–134. doi:10.1023/A:1007692713085. S2CID 686980. Archived (PDF) from the original on 2022-10-09.
Rish, Irina (2001). An empirical study of the naive Bayes classifier (PDF). IJCAI Workshop on Empirical Methods in AI. Archived (PDF) from the original on 2022-10-09.

ibm.com

research.ibm.com

Rish, Irina (2001). An empirical study of the naive Bayes classifier (PDF). IJCAI Workshop on Empirical Methods in AI. Archived (PDF) from the original on 2022-10-09.

jstor.org

Hand, D. J.; Yu, K. (2001). "Idiot's Bayes — not so stupid after all?". International Statistical Review. 69 (3): 385–399. doi:10.2307/1403452. ISSN 0306-7734. JSTOR 1403452.

kamalnigam.com

McCallum, Andrew; Nigam, Kamal (1998). A comparison of event models for Naive Bayes text classification (PDF). AAAI-98 workshop on learning for text categorization. Vol. 752. Archived (PDF) from the original on 2022-10-09.
Nigam, Kamal; McCallum, Andrew; Thrun, Sebastian; Mitchell, Tom (2000). "Learning to classify text from labeled and unlabeled documents using EM" (PDF). Machine Learning. 39 (2/3): 103–134. doi:10.1023/A:1007692713085. S2CID 686980. Archived (PDF) from the original on 2022-10-09.

mit.edu

people.csail.mit.edu

Rennie, J.; Shih, L.; Teevan, J.; Karger, D. (2003). Tackling the poor assumptions of naive Bayes classifiers (PDF). ICML. Archived (PDF) from the original on 2022-10-09.

nips.cc

papers.nips.cc

Ng, Andrew Y.; Jordan, Michael I. (2002). On discriminative vs. generative classifiers: A comparison of logistic regression and naive Bayes. NIPS. Vol. 14.

psu.edu

citeseerx.ist.psu.edu

Caruana, R.; Niculescu-Mizil, A. (2006). An empirical comparison of supervised learning algorithms. Proc. 23rd International Conference on Machine Learning. CiteSeerX 10.1.1.122.5901.

researchgate.net

Metsis, Vangelis; Androutsopoulos, Ion; Paliouras, Georgios (2006). Spam filtering with Naive Bayes—which Naive Bayes?. Third conference on email and anti-spam (CEAS). Vol. 17.

semanticscholar.org

api.semanticscholar.org

Piryonesi, S. Madeh; El-Diraby, Tamer E. (2020-06-01). "Role of Data Analytics in Infrastructure Asset Management: Overcoming Data Size and Quality Problems". Journal of Transportation Engineering, Part B: Pavements. 146 (2): 04020022. doi:10.1061/JPEODX.0000175. S2CID 216485629.
Nigam, Kamal; McCallum, Andrew; Thrun, Sebastian; Mitchell, Tom (2000). "Learning to classify text from labeled and unlabeled documents using EM" (PDF). Machine Learning. 39 (2/3): 103–134. doi:10.1023/A:1007692713085. S2CID 686980. Archived (PDF) from the original on 2022-10-09.

stackexchange.com

stats.stackexchange.com

"Why does Naive Bayes work better when the number of features >> sample size compared to more sophisticated ML algorithms?". Cross Validated Stack Exchange. Retrieved 24 January 2023.

umass.edu

people.cs.umass.edu

McCallum, Andrew. "Graphical Models, Lecture2: Bayesian Network Representation" (PDF). Archived (PDF) from the original on 2022-10-09. Retrieved 22 October 2019.

unb.ca

cs.unb.ca

Zhang, Harry. The Optimality of Naive Bayes (PDF). FLAIRS2004 conference.

web.archive.org

Niculescu-Mizil, Alexandru; Caruana, Rich (2005). Predicting good probabilities with supervised learning (PDF). ICML. doi:10.1145/1102351.1102430. Archived from the original (PDF) on 2014-03-11. Retrieved 2016-04-24.

worldcat.org

search.worldcat.org

Hand, D. J.; Yu, K. (2001). "Idiot's Bayes — not so stupid after all?". International Statistical Review. 69 (3): 385–399. doi:10.2307/1403452. ISSN 0306-7734. JSTOR 1403452.
Hastie, Trevor. (2001). The elements of statistical learning : data mining, inference, and prediction : with 200 full-color illustrations. Tibshirani, Robert., Friedman, J. H. (Jerome H.). New York: Springer. ISBN 0-387-95284-5. OCLC 46809224.

wustl.edu

machinelearning.wustl.edu

Niculescu-Mizil, Alexandru; Caruana, Rich (2005). Predicting good probabilities with supervised learning (PDF). ICML. doi:10.1145/1102351.1102430. Archived from the original (PDF) on 2014-03-11. Retrieved 2016-04-24.