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- W2115021872 abstract "Most classification methods assume that the samples are drawn independently and identically from an unknown data generating distribution, yet this assumption is violated in several real life problems. In order to relax this assumption, we consider the case where batches or groups of samples may have internal correlations, whereas the samples from different batches may be considered to be uncorrelated. Two algorithms are developed to classify all the samples in a batch jointly, one based on a probabilistic analysis and another based on a mathematical programming approach. Experiments on three real-life computer aided diagnosis (CAD) problems demonstrate that the proposed algorithms are significantly more accurate than a naive SVM which ignores the correlations among the samples." @default.
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- W2115021872 date "2006-01-01" @default.
- W2115021872 modified "2023-09-22" @default.
- W2115021872 title "Batch Classification with Applications in Computer Aided Diagnosis" @default.
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- W2115021872 doi "https://doi.org/10.1007/11871842_43" @default.
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