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Adaptive Information Filtering is a challenging computational problem that requires a high dimensional feature space. However, theoretical issues arise when vector-based representations are adopted in such a space. In this paper, we use AIF as a test bed to provide experimental evidence indicating that the learning abilities of vector-based Artificial Immune Systems are diminished in a high dimensional space.
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