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In this paper, we propose Semi-Supervised pLSA(SSpLSA) for image classification. Compared with the classic non-supervised pLSA, our method overcomes the shortcoming of poor classification performance when the features of two categories are quite similar. By introducing category label information into EM algorithm, the iteration process can be directed carefully to the desired result. SS-pLSA greatly...
One of the main drawbacks of boosting is its overfitting and poor predictive accuracy when the training dataset is small and imbalanced. In this paper, we introduce a novel learning algorithm Boost-BFKO, which combines boosting and data generation. It is suitable for small and imbalanced training datasets. To enlarge training sets, Boost-BFKO uses the adaptive Balanced Feature Knockout procedure (BFKO)...
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