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Exploiting the structure of feature spaces in kernel learning

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Table 2.2: Datasets information: name, source, number of features and number of examples.
Figure 5.1: KOMD solutions of the first phase found using different Λ in a simple toy classification problem
Figure 7.1: Value of the quality function (Separation) versus the AUC obtained using the greedy algorithm over the Splice dataset.
Table 7.3: AUC ±std results on three datasets of various MKL methods and baselines (RBF feature subset d = 5).
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