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- W2133604244 abstract "All positive examples are alike; each negative example is negative in its own way. During interactive multimedia information retrieval, the number of training samples fed-back by the user is usually small; furthermore, they are not representative for the true distributions-especially the negative examples. Adding to the difficulties is the nonlinearity in real-world distributions. Existing solutions fail to address these problems in a principled way. This paper proposes biased discriminant analysis and transforms specifically designed to address the asymmetry between the positive and negative examples, and to trade off generalization for robustness under a small training sample. The kernel version, namely BiasMap , is derived to facilitate nonlinear biased discrimination. Extensive experiments are carried out for performance evaluation as compared to the state-of-the-art methods." @default.
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- W2133604244 date "2005-08-25" @default.
- W2133604244 modified "2023-09-24" @default.
- W2133604244 title "Small sample learning during multimedia retrieval using BiasMap" @default.
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- W2133604244 doi "https://doi.org/10.1109/cvpr.2001.990450" @default.
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