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Davis King 2012-05-19 20:03:11 -04:00
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dlib/svm/structural_svm_graph_labeling_problem_abstract.h
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@ -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