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- W1997120353 abstract "We propose a new distance estimation technique by boosting and apply it to enhance the effectiveness of classifier whenthe training set is insufficient. The proposed method is called Boosted Distance based on local and global dissimilarityrepresentation (BDLGDR). It is a modified method of Boosted Distance. Rather than simply differentiating the featurevectors, we calculate a new dissimilarity representation of each couple of feature vectors. This new dissimilarityrepresentation contains two parts: local dissimilarity representation part and global dissimilarity representation part. Theproposed method does not only achieve high classification accuracy when the training set is insufficient but when thenumber of training set is sufficient it also can achieve as high accuracy as AdaBoost. The method has been thoroughlytested on several databases of high-resolution (1.25m) Terra-SAR images. In the first experiment, we decreased thenumber of the training sample per class from 10 to 1. The result showed that the proposed method outperformed bothBoosted Distance and AdaBoost. In the second experiment, we used sufficient training samples. The experimental resultilluminated that the proposed method performed at least as well as AdaBoost and needed fewer iteration rounds toconverge than Boosted Distance." @default.
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- W1997120353 date "2009-10-30" @default.
- W1997120353 modified "2023-09-24" @default.
- W1997120353 title "Boosted distance based on local and global dissimilarity representation" @default.
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- W1997120353 doi "https://doi.org/10.1117/12.833006" @default.
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