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DOC Use :doi: and :arxiv: directives for references (#21099)
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‎doc/about.rst

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If you want to cite scikit-learn for its API or design, you may also want to consider the
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following paper:
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`API design for machine learning software: experiences from the scikit-learn
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project <https://arxiv.org/abs/1309.0238>`_, Buitinck *et al.*, 2013.
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:arxiv:`API design for machine learning software: experiences from the scikit-learn
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project <1309.0238>`, Buitinck *et al.*, 2013.
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Bibtex entry::
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‎doc/conf.py

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"add_toctree_functions",
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"sphinx-prompt",
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"sphinxext.opengraph",
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"doi_role",
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]
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# Support for `plot::` directives in sphinx 3.2 requires matplotlib 3.1.0 or newer

‎doc/modules/clustering.rst

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<http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.19.8100>`_
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Andrew Y. Ng, Michael I. Jordan, Yair Weiss, 2001
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* `"Preconditioned Spectral Clustering for Stochastic
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* :arxiv:`"Preconditioned Spectral Clustering for Stochastic
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Block Partition Streaming Graph Challenge"
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<https://arxiv.org/abs/1708.07481>`_
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<1309.0238>`
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David Zhuzhunashvili, Andrew Knyazev
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.. _hierarchical_clustering:
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.. topic:: References
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* Peter J. Rousseeuw (1987). "Silhouettes: a Graphical Aid to the
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Interpretation and Validation of Cluster Analysis". Computational
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and Applied Mathematics 20: 53–65.
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`doi:10.1016/0377-0427(87)90125-7 <https://doi.org/10.1016/0377-0427(87)90125-7>`_.
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* Peter J. Rousseeuw (1987). :doi:`"Silhouettes: a Graphical Aid to the
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Interpretation and Validation of Cluster Analysis"<10.1016/0377-0427(87)90125-7>`
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. Computational and Applied Mathematics 20: 53–65.
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Advantages
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* Caliński, T., & Harabasz, J. (1974).
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`"A Dendrite Method for Cluster Analysis"
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<https://www.researchgate.net/publication/233096619_A_Dendrite_Method_for_Cluster_Analysis>`_.
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Communications in Statistics-theory and Methods 3: 1-27.
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`doi:10.1080/03610927408827101 <https://doi.org/10.1080/03610927408827101>`_.
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:doi:`Communications in Statistics-theory and Methods 3: 1-27 <10.1080/03610927408827101>`.
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.. _davies-bouldin_index:
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.. topic:: References
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* Davies, David L.; Bouldin, Donald W. (1979).
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"A Cluster Separation Measure"
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:doi:`"A Cluster Separation Measure" <10.1109/TPAMI.1979.4766909>`
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IEEE Transactions on Pattern Analysis and Machine Intelligence.
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PAMI-1 (2): 224-227.
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`doi:10.1109/TPAMI.1979.4766909 <https://doi.org/10.1109/TPAMI.1979.4766909>`_.
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* Halkidi, Maria; Batistakis, Yannis; Vazirgiannis, Michalis (2001).
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"On Clustering Validation Techniques"
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:doi:`"On Clustering Validation Techniques" <10.1023/A:1012801612483>`
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Journal of Intelligent Information Systems, 17(2-3), 107-145.
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`doi:10.1023/A:1012801612483 <https://doi.org/10.1023/A:1012801612483>`_.
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* `Wikipedia entry for Davies-Bouldin index
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<https://en.wikipedia.org/wiki/Davies–Bouldin_index>`_.

‎doc/modules/decomposition.rst

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.. topic:: References:
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* Algorithm 4.3 in
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`"Finding structure with randomness: Stochastic algorithms for
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:arxiv:`"Finding structure with randomness: Stochastic algorithms for
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constructing approximate matrix decompositions"
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<https://arxiv.org/abs/0909.4061>`_
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<0909.4061>`
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Halko, et al., 2009
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* `"An implementation of a randomized algorithm for principal component
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* *randomized* solver:
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- Algorithm 4.3 in
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`"Finding structure with randomness: Stochastic algorithms for
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* Algorithm 4.3 in
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:arxiv:`"Finding structure with randomness: Stochastic algorithms for
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constructing approximate matrix decompositions"
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<https://arxiv.org/abs/0909.4061>`_
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<0909.4061>`
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Halko, et al., 2009
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- `"An implementation of a randomized algorithm for principal component
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* `"An implementation of a randomized algorithm for principal component
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analysis"
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<https://arxiv.org/pdf/1412.3510.pdf>`_
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A. Szlam et al. 2014

‎doc/modules/ensemble.rst

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<https://statweb.stanford.edu/~jhf/ftp/stobst.pdf>`_
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.. [R2007] G. Ridgeway, "Generalized Boosted Models: A guide to the gbm
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package", 2007
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.. [XGBoost] Tianqi Chen, Carlos Guestrin, `"XGBoost: A Scalable Tree
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Boosting System" <https://arxiv.org/abs/1603.02754>`_
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.. [XGBoost] Tianqi Chen, Carlos Guestrin, :arxiv:`"XGBoost: A Scalable Tree
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Boosting System" <1603.02754>`
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.. [LightGBM] Ke et. al. `"LightGBM: A Highly Efficient Gradient
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BoostingDecision Tree" <https://papers.nips.cc/paper/
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6907-lightgbm-a-highly-efficient-gradient-boosting-decision-tree>`_

‎doc/modules/grid_search.rst

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Optimization <http://proceedings.mlr.press/v51/jamieson16.html>`_, in
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proc. of Machine Learning Research, 2016.
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.. [2] L. Li, K. Jamieson, G. DeSalvo, A. Rostamizadeh, A. Talwalkar,
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`Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization
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<https://arxiv.org/abs/1603.06560>`_, in Machine Learning Research
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18, 2018.
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:arxiv:`Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization
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<1603.06560>`, in Machine Learning Research 18, 2018.
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.. _grid_search_tips:
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‎doc/modules/kernel_approximation.rst

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.. [VVZ2010] `"Generalized RBF feature maps for Efficient Detection"
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<https://www.robots.ox.ac.uk/~vgg/publications/2010/Sreekanth10/sreekanth10.pdf>`_
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Vempati, S. and Vedaldi, A. and Zisserman, A. and Jawahar, CV - 2010
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.. [PP2013] `"Fast and scalable polynomial kernels via explicit feature maps"
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<https://doi.org/10.1145/2487575.2487591>`_
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.. [PP2013] :doi:`"Fast and scalable polynomial kernels via explicit feature maps"
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<10.1145/2487575.2487591>`
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Pham, N., & Pagh, R. - 2013
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.. [CCF2002] `"Finding frequent items in data streams"
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<http://www.cs.princeton.edu/courses/archive/spring04/cos598B/bib/CharikarCF.pdf>`_

‎doc/modules/linear_model.rst

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.. [6] Mark Schmidt, Nicolas Le Roux, and Francis Bach: `Minimizing Finite Sums with the Stochastic Average Gradient. <https://hal.inria.fr/hal-00860051/document>`_
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.. [7] Aaron Defazio, Francis Bach, Simon Lacoste-Julien: `SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives. <https://arxiv.org/abs/1407.0202>`_
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.. [7] Aaron Defazio, Francis Bach, Simon Lacoste-Julien:
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:arxiv:`SAGA: A Fast Incremental Gradient Method With Support for
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Non-Strongly Convex Composite Objectives. <1407.0202>`
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.. [8] https://en.wikipedia.org/wiki/Broyden%E2%80%93Fletcher%E2%80%93Goldfarb%E2%80%93Shanno_algorithm
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<https://gib.people.uic.edu/RQ.pdf>`_
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Econometrica: journal of the Econometric Society, 33-50.
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* Portnoy, S., & Koenker, R. (1997). The Gaussian hare and the Laplacian
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* Portnoy, S., & Koenker, R. (1997). :doi:`The Gaussian hare and the Laplacian
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tortoise: computability of squared-error versus absolute-error estimators.
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Statistical Science, 12, 279-300. https://doi.org/10.1214/ss/1030037960
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Statistical Science, 12, 279-300 <10.1214/ss/1030037960>`.
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* Koenker, R. (2005). Quantile Regression.
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Cambridge University Press. https://doi.org/10.1017/CBO9780511754098
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* Koenker, R. (2005). :doi:`Quantile Regression <10.1017/CBO9780511754098>`.
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Cambridge University Press.
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.. _polynomial_regression:

‎doc/modules/model_evaluation.rst

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Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples,
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2015.
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.. [Urbanowicz2015] Urbanowicz R.J., Moore, J.H. `ExSTraCS 2.0: description and evaluation of a scalable learning
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classifier system <https://doi.org/10.1007/s12065-015-0128-8>`_, Evol. Intel. (2015) 8: 89.
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.. [Urbanowicz2015] Urbanowicz R.J., Moore, J.H. :doi:`ExSTraCS 2.0: description
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and evaluation of a scalable learning classifier
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system <10.1007/s12065-015-0128-8>`, Evol. Intel. (2015) 8: 89.
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.. _cohen_kappa:
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‎doc/modules/neural_networks_supervised.rst

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MLP trains using `Stochastic Gradient Descent
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`Adam <https://arxiv.org/abs/1412.6980>`_, or
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:arxiv:`Adam <1412.6980>`, or
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`L-BFGS <https://en.wikipedia.org/wiki/Limited-memory_BFGS>`__.
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Stochastic Gradient Descent (SGD) updates parameters using the gradient of the
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loss function with respect to a parameter that needs adaptation, i.e.

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