clarified spec

dlib/svm/structural_svm_graph_labeling_problem_abstract.h

@@ -33,14 +33,13 @@ namespace dlib
         ensures
             - Note that a graph labeling problem is a task to learn a binary classifier which 
               predicts the correct label for each node in the provided graphs.  Additionally, 
-              we have information in the form of graph edges between nodes where edges are 
-              present when we believe the linked nodes are likely to have the same label.  
-              Therefore, part of a graph labeling problem is to learn to score each edge in 
-              terms of how strongly the edge should enforce labeling consistency between 
-              its two nodes.  Thus, to be a valid graph labeling problem, samples should contain 
-              example graphs of connected nodes while labels should indicate the desired 
-              label of each node.  The precise requirements for a valid graph labeling 
-              problem are listed below.
+              we have information in the form of edges between nodes where edges are present 
+              when we believe the linked nodes are likely to have the same label.  Therefore, 
+              part of a graph labeling problem is to learn to score each edge in terms of how 
+              strongly the edge should enforce labeling consistency between its two nodes.  
+              Thus, to be a valid graph labeling problem, samples should contain example graphs 
+              of connected nodes while labels should indicate the desired label of each node.  
+              The precise requirements for a valid graph labeling problem are listed below.
             - This function returns true if all of the following are true and false otherwise:
                 - is_learning_problem(samples, labels) == true
                 - All the vectors stored on the edges of each graph in samples