Ditemukan 2 dokumen yang sesuai dengan query
Markovsky, Ivan
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Data approximation by low-complexity models details the theory, algorithms, and applications of structured low-rank approximation. Efficient local optimization methods and effective suboptimal convex relaxations for Toeplitz, Hankel, and Sylvester structured problems are presented. Much of the text is devoted to describing the applications of the theory including, system and control theory, signal processing, computer algebra for approximate factorization and common divisor computation, computer vision for image deblurring and segmentation, machine learning for information retrieval and ...
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London: [, Springer], 2012
e20410845
eBooks Universitas Indonesia Library
Markovsky, Ivan
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Exact and Approximate Modeling of Linear Systems: A Behavioral Approach elegantly introduces the behavioral approach to mathematical modeling, an approach that requires models to be viewed as sets of possible outcomes rather than to be a priori bound to particular representations. The authors discuss exact and approximate fitting of data by linear, bilinear, and quadratic static models and linear dynamic models, a formulation that enables readers to select the most suitable representation for a particular ...
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Philadelphia: Society for Industrial and Applied Mathematics, 2006
e20451308
eBooks Universitas Indonesia Library
