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Multiple-instance active learning (MIAL) is a paradigm to collect sufficient training bags for a multiple-instance learning (MIL) problem, by selecting and querying the most valuable unlabeled bags iteratively. Existing works on MIAL evaluate an unlabeled bag by its informativeness with regard to the current classifier, but neglect the internal distribution of its instances, which can reflect the...
Traditional nonnegative matrix factorization (NMF) is an unsupervised method for linear feature extraction. Recently, NMF with block strategy is shown to be able to extract more sparse and discriminative information of the images. To enhance the discriminative power of NMF, this paper proposes a block kernel nonnegative matrix factorization (BKNMF) based on the kernel theory and block technique. Kernel...
Traditional Nonnegative Matrix Factorization (NMF) is a linear and unsupervised algorithm. This would limit the classification power of NMF for the complicated data. To overcome the above limitations of NMF, this paper proposes a novel supervised and nonlinear NMF algorithm based on kernel theory and discriminant analysis. We incorporate the class label information into the decomposition of NMF in...
Conventional steganalysis method generally encounters the problems of embedding algorithm mismatch (EAM) and cover source mismatch (CSM). These problems cause difficulties in the use of steganalysis in the real world. Learning from the idea of image pre-classified, this study presents a JPEG steganalysis paradigm combining the similarity retrieval of image-inherent statistical properties (IISP) and...
In view of the difficulty of predicting engine performance effectively in traditional methods, a prediction method based on CFPNN (Cascade-Forward Process Neural Network) is proposed. By introducing a set of appropriate orthogonal basis functions into the input space, the input functions and weight functions are expanded. The time aggregation operation of the process neurons is simplified by this...
A water quantities allocation arithmetic was proposed, Radial basis function neural network (RBFNN) was designed, and simulated annealing arithmetic was adopted to adjust the network weights. MATLAB program was compiled; experiments on related data have been done employing the program. All experiments have shown that the arithmetic can efficiently approach the surface with 10-4 mm error precision,...
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