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Feature selection (FS) and classifier design (CD) are two basic stages in the construction of a classification system. Typically, both tasks have been studied separately in literature. FS aims to remove irrelevant and redundant features whereas CD generates a prediction rule for classifying patterns whose class is unknown. Despite the relationship between FS and CD with radial basis function networks...
We apply our recently developed concept of mutual exclusivity [1] in the context of discriminative coding, to the problem of learning dictionary for representing signals drawn from N classes in a way that optimizes their discriminability. We first briefly review our mutual-exclusivity concept and then deploy it a simple discriminative dictionary learning algorithm that directly generalizes the well-known...
One of the objectives of designing feature selection learning algorithms is to obtain classifiers that depend on a small number of attributes and have verifiable future performance guarantees. There are few, if any, approaches that successfully address the two goals simultaneously. To the best of our knowledge, such algorithms that give theoretical bounds on the future performance have not been proposed...
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