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Learning Discriminative Tree Edit Similarities for Linear Classification - Application to Melody Recognition

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Figure

Figure 2: An optimal edit script according to Selkow tree edit distance al- al-gorithm Selkow (1977)
Figure 2: Linear separator α learned from a training set of trees {(A, +1), (B, +1), (C, +1), (D, +1), (E, −1), (F, −1), (G, −1), (H, −1)} thanks to an (, γ, τ )-good similarity function
Figure 3: Above: tree representation of a one-bar melody with an example of how pitch labels are propagated
Table 1: Success rates (%) and standard deviation obtained from the five edit similarities in 1NN and linear classifications on the Pascal corpus.
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