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Computational Statistics and Machine Learning: A Sparse Approach

Computational Statistics and Machine Learning: A Sparse Approach focuses on using sparse algorithms in statistics and machine learning. The first part addresses the L_0 norm minimization using greedy algorithms and considers the set covering machines, matching pursuit algorithms in machine learning, and random projection methods. The second part, which addresses L_1 norm minimization, discusses linear programming boosting, LASSO/LARS, and compressed sensing. All chapters include a detailed description of algorithms and pseudo-code and, where appropriate, a theoretical analysis of generalization ability motivating the use of sparsity. A final chapter covers applications.

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Computational Statistics and Machine Learning: A Sparse Approach
Kategorie: Technologie
Kategorie BISAC:
Mathematics > Prawdopodobieństwo i statystyka
Wydawca: John Wiley & Sons
Seria wydawnicza: Wiley Series in Probability and Statistics
Język: Angielski
ISBN-13: 9780470973561
Rok wydania: 2018
Ilość stron: 352
Oprawa: Twarda
Wolumenów: 01
Computational Statistics and Machine Learning: A Sparse Approach focuses on using sparse algorithms in statistics and machine learning. The first part addresses the L—0 norm minimization using greedy algorithms and considers the set covering machines, matching pursuit algorithms in machine learning, and random projection methods.





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