Fixed some typos in the spec files.

--HG--
extra : convert_revision : svn%3Afdd8eb12-d10e-0410-9acb-85c331704f74/trunk%402963

dlib/svm/svm_abstract.h

@@ -325,7 +325,7 @@ namespace dlib
               given binary classification problem for the given number of folds.
               Each fold is tested using the output of the trainer and the average 
               classification accuracy from all folds is returned.  
-            - The accuracy is returned in a column vector, let us call it R.  Both 
+            - The accuracy is returned in a row vector, let us call it R.  Both 
               quantities in R are numbers between 0 and 1 which represent the fraction 
               of examples correctly classified.  R(0) is the fraction of +1 examples 
               correctly classified and R(1) is the fraction of -1 examples correctly 
@@ -355,7 +355,7 @@ namespace dlib
             - dec_funct_type == some kind of decision function object (e.g. decision_function)
         ensures
             - tests the given decision function by calling on the x_test and y_test samples.
-            - The test accuracy is returned in a column vector, let us call it R.  Both 
+            - The test accuracy is returned in a row vector, let us call it R.  Both 
               quantities in R are numbers between 0 and 1 which represent the fraction 
               of examples correctly classified.  R(0) is the fraction of +1 examples 
               correctly classified and R(1) is the fraction of -1 examples correctly 

dlib/svm/svm_threaded_abstract.h

@@ -37,7 +37,7 @@ namespace dlib
               Each fold is tested using the output of the trainer and the average 
               classification accuracy from all folds is returned.  
             - uses num_threads threads of execution in doing the cross validation.  
-            - The accuracy is returned in a column vector, let us call it R.  Both 
+            - The accuracy is returned in a row vector, let us call it R.  Both 
               quantities in R are numbers between 0 and 1 which represent the fraction 
               of examples correctly classified.  R(0) is the fraction of +1 examples 
               correctly classified and R(1) is the fraction of -1 examples correctly