Tatyana V. Bandos Marsheva

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This paper presents a semi-supervised graph-based method for the classification of hyperspectral images. The method is designed to handle the special characteristics of hyperspectral images, namely high input dimension of pixels, low number of labeled samples, and spatial variability of the spectral signature. To alleviate these problems, the method(More)
This paper analyzes the classification of hyperspectral remote sensing images with linear discriminant analysis (LDA) in the presence of a small ratio between the number of training samples and the number of spectral features. In these particular ill-posed problems, a reliable LDA requires one to introduce regularization for problem solving. Nonetheless, in(More)
This paper proposes the use of statistical criteria for early stopping Support Vector Machines, both for regression and classification problems. The method basically stops the minimization of the primal functional when moments of the error signal (up to forth order) become stationary, rather than according to a tolerance threshold of primal convergence(More)
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