diff --git a/examples/svm_ex.cpp b/examples/svm_ex.cpp
index d6fbe4fec..1a8d9d737 100644
--- a/examples/svm_ex.cpp
+++ b/examples/svm_ex.cpp
@@ -220,15 +220,15 @@ int main()
 
 
     // Lastly, note that the decision functions we trained above involved well over 100 
-    // support vectors.  Support vector machines in general tend to find decision functions
-    // that involve a lot of support vectors.  This is significant because the more 
-    // support vectors in a decision function, the longer it takes to classify new examples.
+    // basis vectors.  Support vector machines in general tend to find decision functions
+    // that involve a lot of basis vectors.  This is significant because the more 
+    // basis vectors in a decision function, the longer it takes to classify new examples.
     // So dlib provides the ability to find an approximation to the normal output of a
-    // support vector machine using fewer support vectors.  
+    // trainer using fewer basis vectors.  
 
     // Here we determine the cross validation accuracy when we approximate the output
-    // using only 10 support vectors.  To do this we use the reduced2() function.  It
-    // takes a trainer object and the number of support vectors to use and returns 
+    // using only 10 basis vectors.  To do this we use the reduced2() function.  It
+    // takes a trainer object and the number of basis vectors to use and returns 
     // a new trainer object that applies the necessary post processing during the creation
     // of decision function objects.
     cout << "\ncross validation accuracy with only 10 support vectors: " 
@@ -239,7 +239,7 @@ int main()
          << cross_validate_trainer(trainer, samples, labels, 3);
 
     // When you run this program you should see that, for this problem, you can reduce 
-    // the number of support vectors down to 10 without hurting the cross validation
+    // the number of basis vectors down to 10 without hurting the cross validation
     // accuracy.