diff --git a/examples/bayes_net_gui_ex.cpp b/examples/bayes_net_gui_ex.cpp
index b4cc9e4dd..70aefc30d 100644
--- a/examples/bayes_net_gui_ex.cpp
+++ b/examples/bayes_net_gui_ex.cpp
@@ -626,6 +626,7 @@ on_menu_help_about (
 {
     message_box("About","This application is the GUI front end to the dlib C++ Library's\n"
                 "Bayesian Network inference utilities\n\n"
+                "Version 1.1\n\n"
                 "See http://dclib.sourceforge.net for updates");
 }
 
diff --git a/examples/svm_ex.cpp b/examples/svm_ex.cpp
index 64efb8758..9bb82941f 100644
--- a/examples/svm_ex.cpp
+++ b/examples/svm_ex.cpp
@@ -144,6 +144,7 @@ int main()
     // We can also train a decision function that reports a well conditioned probability instead of just a number
     // > 0 for the +1 class and < 0 for the -1 class.  An example of doing that follows:
     probabilistic_decision_function<kernel_type> learned_probabilistic_decision_function = svm_nu_train_prob(samples, labels, kernel_type(0.1), 0.1, 3);
+    // Now we have a function that returns the probability that a given sample is of the +1 class.  
 
     // print out the number of support vectors in the resulting decision function.  (it should be the same as in the one above)
     cout << "\nnumber of support vectors in our learned_probabilistic_decision_function is "