General Algorithm

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This program implements matrix optimization in compressed sensing theory using alternating projection methods. The algorithm minimizes coherence between sampling and sparse matrices by iteratively projecting onto structural and incoherence constraint

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Pattern Recognition Toolbox Functions featuring multiple recognition methods including KNN, PCA, SVM, C4.5, EM algorithm with code implementation details and technical specifications

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A classic application of Kalman filtering involves predicting an object's position coordinates and velocity from a limited sequence of noisy observations. This algorithm finds extensive applications in various engineering fields including radar syste

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