scikit-learn · glemaitre · Nov 2, 2022 · Nov 1, 2022 · Nov 1, 2022 · Nov 1, 2022
diff --git a/doc/modules/clustering.rst b/doc/modules/clustering.rst
@@ -563,11 +563,11 @@ graph, and SpectralClustering is initialized with `affinity='precomputed'`::
    Jianbo Shi, Jitendra Malik, 2000
 
  * `"A Random Walks View of Spectral Segmentation"
-   <http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.33.1501>`_
+   <https://citeseerx.ist.psu.edu/doc_view/pid/84a86a69315e994cfd1e0c7debb86d62d7bd1f44>`_
    Marina Meila, Jianbo Shi, 2001
 
  * `"On Spectral Clustering: Analysis and an algorithm"
-   <http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.19.8100>`_
+   <https://citeseerx.ist.psu.edu/doc_view/pid/796c5d6336fc52aa84db575fb821c78918b65f58>`_
    Andrew Y. Ng, Michael I. Jordan, Yair Weiss, 2001
 
  * :arxiv:`"Preconditioned Spectral Clustering for Stochastic

diff --git a/doc/modules/feature_selection.rst b/doc/modules/feature_selection.rst
@@ -305,7 +305,7 @@ fit and requires no iterations.
 
    .. [sfs] Ferri et al, `Comparative study of techniques for
       large-scale feature selection
-      <http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.24.4369&rep=rep1&type=pdf>`_.
+      <https://citeseerx.ist.psu.edu/doc_view/pid/5fedabbb3957bbb442802e012d829ee0629a01b6>`_.
 
 Feature selection as part of a pipeline
 =======================================

diff --git a/doc/modules/linear_model.rst b/doc/modules/linear_model.rst
@@ -794,7 +794,7 @@ is more robust to ill-posed problems.
 
     * Section 3.3 in Christopher M. Bishop: Pattern Recognition and Machine Learning, 2006
 
-    * David J. C. MacKay, `Bayesian Interpolation <http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.27.9072&rep=rep1&type=pdf>`_, 1992.
+    * David J. C. MacKay, `Bayesian Interpolation <https://citeseerx.ist.psu.edu/doc_view/pid/b14c7cc3686e82ba40653c6dff178356a33e5e2c>`_, 1992.
 
     * Michael E. Tipping, `Sparse Bayesian Learning and the Relevance Vector Machine <http://www.jmlr.org/papers/volume1/tipping01a/tipping01a.pdf>`_, 2001.
 
@@ -836,11 +836,11 @@ Ridge Regression`_, see the example below.
 
     .. [1] Christopher M. Bishop: Pattern Recognition and Machine Learning, Chapter 7.2.1
 
-    .. [2] David Wipf and Srikantan Nagarajan: `A new view of automatic relevance determination <https://papers.nips.cc/paper/3372-a-new-view-of-automatic-relevance-determination.pdf>`_
+    .. [2] David Wipf and Srikantan Nagarajan: `A New View of Automatic Relevance Determination <https://papers.nips.cc/paper/3372-a-new-view-of-automatic-relevance-determination.pdf>`_
 
     .. [3] Michael E. Tipping: `Sparse Bayesian Learning and the Relevance Vector Machine <http://www.jmlr.org/papers/volume1/tipping01a/tipping01a.pdf>`_
 
-    .. [4] Tristan Fletcher: `Relevance Vector Machines explained <http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.651.8603&rep=rep1&type=pdf>`_
+    .. [4] Tristan Fletcher: `Relevance Vector Machines Explained <https://citeseerx.ist.psu.edu/doc_view/pid/3dc9d625404fdfef6eaccc3babddefe4c176abd4>`_
 
 
 .. _Logistic_regression:

diff --git a/doc/modules/manifold.rst b/doc/modules/manifold.rst
@@ -268,7 +268,7 @@ The overall complexity of MLLE is
 .. topic:: References:
 
    * `"MLLE: Modified Locally Linear Embedding Using Multiple Weights"
-     <http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.70.382>`_
+     <https://citeseerx.ist.psu.edu/doc_view/pid/0b060fdbd92cbcc66b383bcaa9ba5e5e624d7ee3>`_
      Zhang, Z. & Wang, J.
 
 

diff --git a/doc/modules/model_evaluation.rst b/doc/modules/model_evaluation.rst
@@ -830,7 +830,7 @@ precision-recall curve as follows.
      2008.
   .. [Everingham2010] M. Everingham, L. Van Gool, C.K.I. Williams, J. Winn, A. Zisserman,
      `The Pascal Visual Object Classes (VOC) Challenge
-     <http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.157.5766&rep=rep1&type=pdf>`_,
+     <https://citeseerx.ist.psu.edu/doc_view/pid/b6bebfd529b233f00cb854b7d8070319600cf59d>`_,
      IJCV 2010.
   .. [Davis2006] J. Davis, M. Goadrich, `The Relationship Between Precision-Recall and ROC Curves
      <https://www.biostat.wisc.edu/~page/rocpr.pdf>`_,

diff --git a/doc/modules/naive_bayes.rst b/doc/modules/naive_bayes.rst
@@ -216,12 +216,12 @@ It is advisable to evaluate both models, if time permits.
 
  * A. McCallum and K. Nigam (1998).
    `A comparison of event models for Naive Bayes text classification.
-   <http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.46.1529>`_
+   <https://citeseerx.ist.psu.edu/doc_view/pid/04ce064505b1635583fa0d9cc07cac7e9ea993cc>`_
    Proc. AAAI/ICML-98 Workshop on Learning for Text Categorization, pp. 41-48.
 
  * V. Metsis, I. Androutsopoulos and G. Paliouras (2006).
    `Spam filtering with Naive Bayes -- Which Naive Bayes?
-   <http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.61.5542>`_
+   <https://citeseerx.ist.psu.edu/doc_view/pid/8bd0934b366b539ec95e683ae39f8abb29ccc757>`_
    3rd Conf. on Email and Anti-Spam (CEAS).
 
 .. _categorical_naive_bayes:

diff --git a/doc/modules/neighbors.rst b/doc/modules/neighbors.rst
@@ -345,8 +345,8 @@ Alternatively, the user can work with the :class:`BallTree` class directly.
 
 .. topic:: References:
 
-   * `"Five balltree construction algorithms"
-     <http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.91.8209>`_,
+   * `"Five Balltree Construction Algorithms"
+     <https://citeseerx.ist.psu.edu/doc_view/pid/17ac002939f8e950ffb32ec4dc8e86bdd8cb5ff1>`_,
      Omohundro, S.M., International Computer Science Institute
      Technical Report (1989)
 

diff --git a/doc/modules/random_projection.rst b/doc/modules/random_projection.rst
@@ -28,7 +28,7 @@ technique for distance based method.
    Kaufmann Publishers Inc., San Francisco, CA, USA, 143-151.
 
  * Ella Bingham and Heikki Mannila. 2001.
-   `Random projection in dimensionality reduction: applications to image and text data. <http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.24.5135&rep=rep1&type=pdf>`_
+   `Random projection in dimensionality reduction: applications to image and text data. <https://citeseerx.ist.psu.edu/doc_view/pid/aed77346f737b0ed5890b61ad02e5eb4ab2f3dc6>`_
    In Proceedings of the seventh ACM SIGKDD international conference on
    Knowledge discovery and data mining (KDD '01). ACM, New York, NY, USA,
    245-250.
@@ -84,7 +84,7 @@ bounded distortion introduced by the random projection::
 
   * Sanjoy Dasgupta and Anupam Gupta, 1999.
     `An elementary proof of the Johnson-Lindenstrauss Lemma.
-    <http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.39.3334&rep=rep1&type=pdf>`_
+    <https://citeseerx.ist.psu.edu/doc_view/pid/95cd464d27c25c9c8690b378b894d337cdf021f9>`_
 
 .. _gaussian_random_matrix:
 

diff --git a/examples/calibration/plot_compare_calibration.py b/examples/calibration/plot_compare_calibration.py
@@ -206,5 +206,5 @@ def predict_proba(self, X):
 #        1996.
 # .. [3] `Obtaining calibrated probability estimates from decision trees and
 #        naive Bayesian classifiers
-#        <http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.29.3039&rep=rep1&type=pdf>`_
+#        <https://citeseerx.ist.psu.edu/doc_view/pid/4f67a122ec3723f08ad5cbefecad119b432b3304>`_
 #        Zadrozny, Bianca, and Charles Elkan. Icml. Vol. 1. 2001.
diff --git a/examples/ensemble/plot_adaboost_regression.py b/examples/ensemble/plot_adaboost_regression.py
@@ -10,7 +10,7 @@
 detail.
 
 .. [1] `H. Drucker, "Improving Regressors using Boosting Techniques", 1997.
-        <http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.31.314>`_
+        <https://citeseerx.ist.psu.edu/doc_view/pid/8d49e2dedb817f2c3330e74b63c5fc86d2399ce3>`_
 
 """
 

diff --git a/sklearn/manifold/_locally_linear.py b/sklearn/manifold/_locally_linear.py
@@ -301,9 +301,9 @@ def locally_linear_embedding(
     .. [2] Donoho, D. & Grimes, C. Hessian eigenmaps: Locally
         linear embedding techniques for high-dimensional data.
         Proc Natl Acad Sci U S A.  100:5591 (2003).
-    .. [3] Zhang, Z. & Wang, J. MLLE: Modified Locally Linear
+    .. [3] `Zhang, Z. & Wang, J. MLLE: Modified Locally Linear
         Embedding Using Multiple Weights.
-        http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.70.382
+        <https://citeseerx.ist.psu.edu/doc_view/pid/0b060fdbd92cbcc66b383bcaa9ba5e5e624d7ee3>`_
     .. [4] Zhang, Z. & Zha, H. Principal manifolds and nonlinear
         dimensionality reduction via tangent space alignment.
         Journal of Shanghai Univ.  8:406 (2004)
@@ -668,9 +668,9 @@ class LocallyLinearEmbedding(
     .. [2] Donoho, D. & Grimes, C. Hessian eigenmaps: Locally
         linear embedding techniques for high-dimensional data.
         Proc Natl Acad Sci U S A.  100:5591 (2003).
-    .. [3] Zhang, Z. & Wang, J. MLLE: Modified Locally Linear
+    .. [3] `Zhang, Z. & Wang, J. MLLE: Modified Locally Linear
         Embedding Using Multiple Weights.
-        http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.70.382
+        <https://citeseerx.ist.psu.edu/doc_view/pid/0b060fdbd92cbcc66b383bcaa9ba5e5e624d7ee3>`_
     .. [4] Zhang, Z. & Zha, H. Principal manifolds and nonlinear
         dimensionality reduction via tangent space alignment.
         Journal of Shanghai Univ.  8:406 (2004)

diff --git a/sklearn/manifold/_spectral_embedding.py b/sklearn/manifold/_spectral_embedding.py
@@ -523,9 +523,9 @@ class SpectralEmbedding(BaseEstimator):
       Ulrike von Luxburg
       <10.1007/s11222-007-9033-z>`
 
-    - On Spectral Clustering: Analysis and an algorithm, 2001
+    - `On Spectral Clustering: Analysis and an algorithm, 2001
       Andrew Y. Ng, Michael I. Jordan, Yair Weiss
-      http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.19.8100
+      <https://citeseerx.ist.psu.edu/doc_view/pid/796c5d6336fc52aa84db575fb821c78918b65f58>`_
 
     - :doi:`Normalized cuts and image segmentation, 2000
       Jianbo Shi, Jitendra Malik

diff --git a/sklearn/mixture/_bayesian_mixture.py b/sklearn/mixture/_bayesian_mixture.py
@@ -323,7 +323,7 @@ class BayesianGaussianMixture(BaseMixture):
     .. [2] `Hagai Attias. (2000). "A Variational Bayesian Framework for
        Graphical Models". In Advances in Neural Information Processing
        Systems 12.
-       <http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.36.2841&rep=rep1&type=pdf>`_
+       <https://citeseerx.ist.psu.edu/doc_view/pid/ee844fd96db7041a9681b5a18bff008912052c7e>`_
 
     .. [3] `Blei, David M. and Michael I. Jordan. (2006). "Variational
        inference for Dirichlet process mixtures". Bayesian analysis 1.1

diff --git a/sklearn/random_projection.py b/sklearn/random_projection.py
@@ -101,9 +101,9 @@ def johnson_lindenstrauss_min_dim(n_samples, *, eps=0.1):
 
     .. [1] https://en.wikipedia.org/wiki/Johnson%E2%80%93Lindenstrauss_lemma
 
-    .. [2] Sanjoy Dasgupta and Anupam Gupta, 1999,
+    .. [2] `Sanjoy Dasgupta and Anupam Gupta, 1999,
            "An elementary proof of the Johnson-Lindenstrauss Lemma."
-           http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.45.3654
+           <https://citeseerx.ist.psu.edu/doc_view/pid/95cd464d27c25c9c8690b378b894d337cdf021f9>`_
 
     Examples
     --------

diff --git a/sklearn/semi_supervised/_label_propagation.py b/sklearn/semi_supervised/_label_propagation.py
@@ -553,9 +553,9 @@ class LabelSpreading(BaseLabelPropagation):
 
     References
     ----------
-    Dengyong Zhou, Olivier Bousquet, Thomas Navin Lal, Jason Weston,
+    `Dengyong Zhou, Olivier Bousquet, Thomas Navin Lal, Jason Weston,
     Bernhard Schoelkopf. Learning with local and global consistency (2004)
-    http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.115.3219
+    <https://citeseerx.ist.psu.edu/doc_view/pid/d74c37aabf2d5cae663007cbd8718175466aea8c>`_
 
     Examples
     --------

diff --git a/sklearn/svm/_classes.py b/sklearn/svm/_classes.py
@@ -753,9 +753,9 @@ class SVC(BaseSVC):
     .. [1] `LIBSVM: A Library for Support Vector Machines
         <http://www.csie.ntu.edu.tw/~cjlin/papers/libsvm.pdf>`_
 
-    .. [2] `Platt, John (1999). "Probabilistic outputs for support vector
-        machines and comparison to regularizedlikelihood methods."
-        <http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.41.1639>`_
+    .. [2] `Platt, John (1999). "Probabilistic Outputs for Support Vector
+        Machines and Comparisons to Regularized Likelihood Methods"
+        <https://citeseerx.ist.psu.edu/doc_view/pid/42e5ed832d4310ce4378c44d05570439df28a393>`_
 
     Examples
     --------
@@ -1017,9 +1017,9 @@ class NuSVC(BaseSVC):
     .. [1] `LIBSVM: A Library for Support Vector Machines
         <http://www.csie.ntu.edu.tw/~cjlin/papers/libsvm.pdf>`_
 
-    .. [2] `Platt, John (1999). "Probabilistic outputs for support vector
-        machines and comparison to regularizedlikelihood methods."
-        <http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.41.1639>`_
+    .. [2] `Platt, John (1999). "Probabilistic Outputs for Support Vector
+        Machines and Comparisons to Regularized Likelihood Methods"
+        <https://citeseerx.ist.psu.edu/doc_view/pid/42e5ed832d4310ce4378c44d05570439df28a393>`_
 
     Examples
     --------
@@ -1233,9 +1233,9 @@ class SVR(RegressorMixin, BaseLibSVM):
     .. [1] `LIBSVM: A Library for Support Vector Machines
         <http://www.csie.ntu.edu.tw/~cjlin/papers/libsvm.pdf>`_
 
-    .. [2] `Platt, John (1999). "Probabilistic outputs for support vector
-        machines and comparison to regularizedlikelihood methods."
-        <http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.41.1639>`_
+    .. [2] `Platt, John (1999). "Probabilistic Outputs for Support Vector
+        Machines and Comparisons to Regularized Likelihood Methods"
+        <https://citeseerx.ist.psu.edu/doc_view/pid/42e5ed832d4310ce4378c44d05570439df28a393>`_
 
     Examples
     --------
@@ -1442,9 +1442,9 @@ class NuSVR(RegressorMixin, BaseLibSVM):
     .. [1] `LIBSVM: A Library for Support Vector Machines
         <http://www.csie.ntu.edu.tw/~cjlin/papers/libsvm.pdf>`_
 
-    .. [2] `Platt, John (1999). "Probabilistic outputs for support vector
-        machines and comparison to regularizedlikelihood methods."
-        <http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.41.1639>`_
+    .. [2] `Platt, John (1999). "Probabilistic Outputs for Support Vector
+        Machines and Comparisons to Regularized Likelihood Methods"
+        <https://citeseerx.ist.psu.edu/doc_view/pid/42e5ed832d4310ce4378c44d05570439df28a393>`_
 
     Examples
     --------

diff --git a/sklearn/tree/_reingold_tilford.py b/sklearn/tree/_reingold_tilford.py
@@ -158,7 +158,7 @@ def ancestor(vil, v, default_ancestor):
     # the relevant text is at the bottom of page 7 of
     # "Improving Walker's Algorithm to Run in Linear Time" by Buchheim et al,
     # (2002)
-    # http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.16.8757&rep=rep1&type=pdf
+    # https://citeseerx.ist.psu.edu/doc_view/pid/1f41c3c2a4880dc49238e46d555f16d28da2940d
     if vil.ancestor in v.parent.children:
         return vil.ancestor
     else: