Merge pull request #82 from yzhao062/jmlr

Jmlr

README.rst

@@ -589,8 +589,6 @@ Reference
 
 .. [#Liu2018Generative] Liu, Y., Li, Z., Zhou, C., Jiang, Y., Sun, J., Wang, M. and He, X., 2018. Generative Adversarial Active Learning for Unsupervised Outlier Detection. arXiv preprint arXiv:1809.10816.
 
-.. [#Ma2003Time] Ma, J. and Perkins, S., 2003, July. Time-series novelty detection using one-class support vector machines. In *IJCNN' 03*\ , pp. 1741-1745. IEEE.
-
 .. [#Papadimitriou2003LOCI] Papadimitriou, S., Kitagawa, H., Gibbons, P.B. and Faloutsos, C., 2003, March. LOCI: Fast outlier detection using the local correlation integral. In *ICDE '03*, pp. 315-326. IEEE.
 
 .. [#Ramakrishnan2019Anomaly] Ramakrishnan, J., Shaabani, E., Li, C. and Sustik, M.A., 2019. Anomaly Detection for an E-commerce Pricing System. arXiv preprint arXiv:1902.09566.

docs/index.rst

@@ -173,7 +173,7 @@ Type                 Abbr              Algorithm
 ===================  ================  ======================================================================================================  =====  ===================================================  ======================================================
 Linear Model         PCA               Principal Component Analysis (the sum of weighted projected distances to the eigenvector hyperplanes)   2003   :class:`pyod.models.pca.PCA`                         :cite:`a-shyu2003novel`
 Linear Model         MCD               Minimum Covariance Determinant (use the mahalanobis distances as the outlier scores)                    1999   :class:`pyod.models.mcd.MCD`                         :cite:`a-rousseeuw1999fast,a-hardin2004outlier`
-Linear Model         OCSVM             One-Class Support Vector Machines                                                                       2003   :class:`pyod.models.ocsvm.OCSVM`                     :cite:`a-scholkopf2001estimating`
+Linear Model         OCSVM             One-Class Support Vector Machines                                                                       2001   :class:`pyod.models.ocsvm.OCSVM`                     :cite:`a-scholkopf2001estimating`
 Proximity-Based      LOF               Local Outlier Factor                                                                                    2000   :class:`pyod.models.lof.LOF`                         :cite:`a-breunig2000lof`
 Proximity-Based      CBLOF             Clustering-Based Local Outlier Factor                                                                   2003   :class:`pyod.models.cblof.CBLOF`                     :cite:`a-he2003discovering`:
 Proximity-Based      LOCI              LOCI: Fast outlier detection using the local correlation integral                                       2003   :class:`pyod.models.loci.LOCI`                       :cite:`a-papadimitriou2003loci`

docs/relevant_knowledge.rst

@@ -24,8 +24,6 @@ In the context of machine learning, there are three common approaches for this t
     - This approach is taken when ground truth is available and it is assumed that outliers will follow the same distribution as in the training set.
     - Any new observations are classified using the model.
 
-The algorithms found in *PyOD* focus on the first two approaches which differ in terms of how the training data is defined and how the model's outputs are interpreted.
-
-If you are interested to know more relevant knowledge,
-refer `Anomaly Detection Resources <https://github.com/yzhao062/anomaly-detection-resources>`_ for
-anomaly detection related books, papers, videos, and toolboxes.
+The algorithms found in *PyOD* focus on the first two approaches which differ in terms of how the training data is defined and how the model's outputs are interpreted. If interested in learning more,
+please refer to our `Anomaly Detection Resources <https://github.com/yzhao062/anomaly-detection-resources>`_ page for
+relevant related books, papers, videos, and toolboxes.