Automated machine learning (English Wikipedia)

Analysis of information sources in references of the Wikipedia article "Automated machine learning" in English language version.

refsWebsite

Global rank English rank

7doi.org

2^nd place

2handle.net

102^nd place

76^th place

1acm.org

1,185^th place

840^th place

1automl.org

low place

1arxiv.org

69^th place

59^th place

1oapen.org

8,298^th place

5,307^th place

1chalearn.org

low place

acm.org (Global: 1,185^th place; English: 840^th place)

dl.acm.org

Thornton C, Hutter F, Hoos HH, Leyton-Brown K (2013). Auto-WEKA: Combined Selection and Hyperparameter Optimization of Classification Algorithms. KDD '13 Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining. pp. 847–855.

arxiv.org (Global: 69^th place; English: 59^th place)

Erickson, Nick; Mueller, Jonas; Shirkov, Alexander; Zhang, Hang; Larroy, Pedro; Li, Mu; Smola, Alexander (2020-03-13). "AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data". arXiv:2003.06505 [stat.ML].

automl.org (Global: low place; English: low place)

icml2014.automl.org

Hutter F, Caruana R, Bardenet R, Bilenko M, Guyon I, Kegl B, and Larochelle H. "AutoML 2014 @ ICML". AutoML 2014 Workshop @ ICML. Retrieved 2018-03-28.^{[permanent dead link]}

chalearn.org (Global: low place; English: low place)

metalearning.chalearn.org

"Meta Learning Challenges". metalearning.chalearn.org. Retrieved 2023-12-03.

doi.org (Global: 2^nd place; English: 2^nd place)

doi.org

Spears, Taylor; Bondo Hansen, Kristian (2023-12-18), "The Use and Promises of Machine Learning in Financial Markets", The Oxford Handbook of the Sociology of Machine Learning, Oxford University Press, doi:10.1093/oxfordhb/9780197653609.013.6, ISBN 978-0-19-765360-9, retrieved 2024-06-10
Olson, R.S., Urbanowicz, R.J., Andrews, P.C., Lavender, N.A., Kidd, L.C., Moore, J.H. (2016). Automating Biomedical Data Science Through Tree-Based Pipeline Optimization. In: Squillero, G., Burelli, P. (eds) Applications of Evolutionary Computation. EvoApplications 2016. Lecture Notes in Computer Science(), vol 9597. Springer, Cham. doi:10.1007/978-3-319-31204-0_9
De Bie, Tijl; De Raedt, Luc; Hernández-Orallo, José; Hoos, Holger H.; Smyth, Padhraic; Williams, Christopher K. I. (March 2022). "Automating Data Science". Communications of the ACM. 65 (3): 76–87. doi:10.1145/3495256. hdl:10251/199907.
Hutter, Frank; Kotthoff, Lars; Vanschoren, Joaquin, eds. (2019). Automated Machine Learning: Methods, Systems, Challenges. The Springer Series on Challenges in Machine Learning. Springer Nature. doi:10.1007/978-3-030-05318-5. hdl:20.500.12657/23012. ISBN 978-3-030-05317-8.
Glover, Ellen (2018). "Machine Learning with Python: Clustering". Built in. doi:10.4135/9781526466426.

dx.doi.org

Spears, Taylor; Bondo Hansen, Kristian (2023-12-18), "The Use and Promises of Machine Learning in Financial Markets", The Oxford Handbook of the Sociology of Machine Learning, Oxford University Press, doi:10.1093/oxfordhb/9780197653609.013.6, ISBN 978-0-19-765360-9, retrieved 2024-06-10
Glover, Ellen (2018). "Machine Learning with Python: Clustering". Built in. doi:10.4135/9781526466426.

handle.net (Global: 102^nd place; English: 76^th place)

hdl.handle.net

De Bie, Tijl; De Raedt, Luc; Hernández-Orallo, José; Hoos, Holger H.; Smyth, Padhraic; Williams, Christopher K. I. (March 2022). "Automating Data Science". Communications of the ACM. 65 (3): 76–87. doi:10.1145/3495256. hdl:10251/199907.
Hutter, Frank; Kotthoff, Lars; Vanschoren, Joaquin, eds. (2019). Automated Machine Learning: Methods, Systems, Challenges. The Springer Series on Challenges in Machine Learning. Springer Nature. doi:10.1007/978-3-030-05318-5. hdl:20.500.12657/23012. ISBN 978-3-030-05317-8.

oapen.org (Global: 8,298^th place; English: 5,307^th place)

library.oapen.org

Hutter, Frank; Kotthoff, Lars; Vanschoren, Joaquin, eds. (2019). Automated Machine Learning: Methods, Systems, Challenges. The Springer Series on Challenges in Machine Learning. Springer Nature. doi:10.1007/978-3-030-05318-5. hdl:20.500.12657/23012. ISBN 978-3-030-05317-8.