update documentation (wip)

README.rst

@@ -145,12 +145,6 @@ FAQ regarding AutoEncoder in PyOD and debugging advice:
 
 **(ii) Outlier Detector/Scores Combination Frameworks**:
 
-#. **Maximization**\ : simply combine scores by taking the maximum across all 
-   base detectors [#Aggarwal2015Theoretical]_
-#. **Average of Maximum (AOM)** [#Aggarwal2015Theoretical]_
-#. **Maximum of Average (MOA)** [#Aggarwal2015Theoretical]_
-#. **Threshold Sum (Thresh)** [#Aggarwal2015Theoretical]_
-
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 Type                 Abbr              Algorithm                                                                                              Year   Ref                                       Link
 ===================  ================  =====================================================================================================  =====  ========================================  ====================================================================================================
@@ -165,14 +159,14 @@ Combination          MOA               Maximization of Average
 
 **(iii) Utility Functions**:
 
-===================  ==================  =====================================================================================================================================================  ======================================================================================================================
+===================  ==================  =====================================================================================================================================================  ==========================================================================================================================
 Type                 Name                Function                                                                                                                                               Link
-===================  ==================  =====================================================================================================================================================  ======================================================================================================================
-Data                 generate_data       Synthesized data generation; normal data is generated by a multivariate Gaussian and outliers are generated by a uniform distribution                  `[generate_data] <https://pyod.readthedocs.io/en/latest/pyod.utils.html#module-pyod.utils.data.generate_data>`_
-Stat                 wpearsonr           Calculate the weighted Pearson correlation of two samples                                                                                              `[wpearsonr] <https://pyod.readthedocs.io/en/latest/pyod.utils.html#module-pyod.utils.stat_models.wpearsonr>`_
-Utility              get_label_n         Turn raw outlier scores into binary labels by assign 1 to top n outlier scores.                                                                        `[get_label_n] <https://pyod.readthedocs.io/en/latest/pyod.utils.html#module-pyod.utils.utility.get_label_n>`_
-Utility              precision_n_scores  calculate precision @ rank n                                                                                                                           `[get_label_n] <https://pyod.readthedocs.io/en/latest/pyod.utils.html#module-pyod.utils.utility.precision_n_scores>`_
-===================  ==================  =====================================================================================================================================================  ======================================================================================================================
+===================  ==================  =====================================================================================================================================================  ==========================================================================================================================
+Data                 generate_data       Synthesized data generation; normal data is generated by a multivariate Gaussian and outliers are generated by a uniform distribution                  `generate_data <https://pyod.readthedocs.io/en/latest/pyod.utils.html#module-pyod.utils.data.generate_data>`_
+Stat                 wpearsonr           Calculate the weighted Pearson correlation of two samples                                                                                              `wpearsonr <https://pyod.readthedocs.io/en/latest/pyod.utils.html#module-pyod.utils.stat_models.wpearsonr>`_
+Utility              get_label_n         Turn raw outlier scores into binary labels by assign 1 to top n outlier scores                                                                         `get_label_n <https://pyod.readthedocs.io/en/latest/pyod.utils.html#module-pyod.utils.utility.get_label_n>`_
+Utility              precision_n_scores  calculate precision @ rank n                                                                                                                           `precision_n_scores <https://pyod.readthedocs.io/en/latest/pyod.utils.html#module-pyod.utils.utility.precision_n_scores>`_
+===================  ==================  =====================================================================================================================================================  ==========================================================================================================================
 
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