Documentation: fixed references for C++ operators

modules/ml/doc/decision_trees.rst

@@ -80,23 +80,21 @@ The structure represents a possible decision tree node split. It has public memb
 
 .. ocv:member:: int[] subset
 
-    Bit array indicating the value subset in case of split on a categorical variable. The rule is:
+    Bit array indicating the value subset in case of split on a categorical variable. The rule is: ::
 
-::
+        if var_value in subset 
+          then next_node <- left 
+          else next_node <- right
 
-    if var_value in subset 
-      then next_node <- left 
-      else next_node <- right
-
-.. ocv:member:: float ord.c 
+.. ocv:member:: float ord::c 
 
     The threshold value in case of split on an ordered variable. The rule is: ::
 
-        if var_value < c 
+        if var_value < ord.c 
           then next_node<-left 
           else next_node<-right
 
-.. ocv:member:: int ord.split_point
+.. ocv:member:: int ord::split_point
 
     Used internally by the training algorithm.
 

modules/ml/doc/neural_networks.rst

@@ -71,7 +71,7 @@ so the error on the test set usually starts increasing after the network
 size reaches a limit. Besides, the larger networks are trained much
 longer than the smaller ones, so it is reasonable to pre-process the data,
 using
-:ocv:func:`PCA::operator()` or similar technique, and train a smaller network
+:ocv:funcx:`PCA::operator()` or similar technique, and train a smaller network
 on only essential features.
 
 Another MPL feature is an inability to handle categorical