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Weight Vector Tuning and Asymptotic Analysis of Binary Linear Classifiers
Applying this method to a number of linear classifiers under a variety of data dimensionality and sample size settings reveals that the classification performance loss due to non-optimal native hyperparameters can be compensated for by weight vector tuning.
Asymptotic Analysis of an Ensemble of Randomly Projected Linear Discriminants
Datasets from the fields of bioinformatics, chemometrics, and face recognition are typically characterized by small samples of high-dimensional data.
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