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- W2394061572 abstract "Combining information from multiple classifiers can improve the performance of pattern recognition systems. However, the traditional methods always assign fixed weights to the classifiers according to their classification performances without considering the sample itself. A clusterirng analogy fusion method is proposed in this paper, which estimates the reliability of each classifier by analyzing the distribution of samples, and adaptively assigns weights to classifiers based on the reliability estimation. This method can be seen as a method lying between feature level and decision level." @default.
- W2394061572 created "2016-06-24" @default.
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- W2394061572 date "2001-01-01" @default.
- W2394061572 modified "2023-09-26" @default.
- W2394061572 title "Adaptive Weighting Based Fusion Method of Multi-Classifiers" @default.
- W2394061572 hasPublicationYear "2001" @default.
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