Figure 3

Ranking SVM. The learning algorithm of the ranking SVM yields a weight vector w that minimizes the pairwise loss dependent on the margin when the training instances are projected onto w. The overall ranking error is reduced to approximate the given ordering in the training set as effectively as possible along w. The principle of margin re-scaling allows for a ranking dependent on the degree of discrepancy in the ranking order and the pairwise loss is influenced by the k-partite ranking error. Therefore, ranking score 2 higher than score 4 is punished with a greater loss than a wrong order of the scores 4 and 3. This is indicated with an increasing margin dependent on the respective scores that are compared with each other.