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This work investigates the discriminative power of wavelet decomposition based texture features in forest cover classification. Our texture features are used as inputs in a random forests classifier. The performances of this tree-based ensemble classifier are assessed by classification accuracy as well as classification confidence provided by an unsupervised version of ensemble margin. The effectiveness...
In remote sensing, where training data are typically ground-based, mislabeled training data is inevitable. This work handles the mislabeling problem by exploiting the ensemble margin for identifying, then eliminating or correcting the mislabeled training data. The effectiveness of our class noise removal and correction methods is demonstrated in performing mapping of land covers. A comparative analysis...
Text documents are often high dimensional and sparse, it is a great challenge to discover the clusters among the unlabelled text data, because there are no obvious clusters by common distance measure. In this paper we present a latent subspace clustering method to find text clusters. In our algorithm, we use latent factors extracted by probability latent semantic analysis (PLSA) to generate latent...
One of the most important link in improves diagnostic accuracy and disease cure rate is accurate classification of disease. The current gene chip's development and widely applications making the diagnosis based on tumor gene expression profiling expected to be on a fast and effective clinical diagnostic method. But the sample of gene is small and the expression data is multi-variable. In this article,...
In this paper, we propose a novel texture feature extraction method which compute the weighted moment of the wavelet energy histogram of the decomposed images. A total of 15 features are extracted for script classification. The author choose six languages (Arabia, Chinese, English, Hindi, Thailand and Korean) to demonstrate the potential of the technology. Experimental results show that the propose...
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