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Learning vector quantization for proximity data

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Figure

Figure 4.1: General framework for non-vectorial LVQ schemes for proximity data.
Table 4.1: Different possible choices when applying LVQ schemes for (dis-)- (dis-)-similarity data.
Figure 4.2: Characteristic spectrum of the considered similarities. The data sets differ as concerns negative eigenvalues corresponding to  non-Euclideanity, and the number of eigenvalues which are different from zero, corresponding to a high dimensional f
Table 4.2: The mean classification error of different classifiers for benchmark data are reported
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