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% Kernel feature extraction. % % gda - Generalized Discriminant Analysis. % greedyappx - Kernel greedy data approximation. % greedykpca - Greedy kernel PCA. % kpca - Kernel Principal
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% Image denoising using kernel PCA. % % make_noisy_data - Adds Gaussian noise to USPS database. % show_denois_tuning - Plots curves of tuning stage of Kernel PCA. % show_denoising - Image d
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% Kernel machines. % % cvkfd - Computes cross validation error for given KFD model. % diagker - Returns diagonal of kernel matrix of given data. % dualcov - Dual representation of cova
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% Demo on Optical Character Recognition (OCR) for handwritten numerals. % % models (dir) - Different SVM models trained for OCR system. % % collect_chars - Collecting training examples for OCR. % mpa
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% Separation of finite sets of vectors % % ekozinec - Kozinec's algorithm for eps-optimal separating hyperplane. % mperceptron - Perceptron algorithm to train linear machine. % perceptron - Perc
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% Algorithms learning linear classifiers from finite vector sets. % % ekozinec - Kozinec's algorithm for eps-optimal separating hyperplane. % ekozinec2 - Kozinec's algorithm for eps-optimal separ
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% Generalized Anderson's task. % % andrerr - Classification error of the Generalized Anderson's task. % androrig - Original method to solve the Anderson's task. % eanders - Epsilon-solutio
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% Algorithms to solve the Generalized Anderson's task. % % andrerr - Classification error of the Generalized Anderson's task. % androrig - Original method to solve the Anderson's task. % ean
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% Linear feature extraction. % % lda - Linear Discriminant Analysis. % pca - Principal Component Analysis. % pcarec - Computes reconstructed vector after PCA projection. % % About: Statistic
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% Linear transformations for feature extraction. % % lda - Linear Discriminant Analysis. % linproj - Linear data projection. % pca - Principal Component Analysis. % % About: Statistica