Irofti Paul (17 results)

Hardcover. Condition: Very Good. 1. Auflage. Unread, some shelfwear. Immediately dispatched from Germany.

Condition: New.

Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book covers all the relevant dictionary learning algorithms, presenting them in full detail and showing their distinct characteristics while also revealing the similarities. It gives implementation tricks that are often ignored but that are crucial for a successful program. Besides MOD, K-SVD, and other standard algorithms, it provides the significant dictionary learning problem variations, such as regularization, incoherence enforcing, finding an economical size, or learning adapted to specific problems like classification. Several types of dictionary structures are treated, including shift invariant; orthogonal blocks or factored dictionaries; and separable dictionaries for multidimensional signals. Nonlinear extensions such as kernel dictionary learning can also be found in the book. The discussion of all these dictionary types and algorithms is enriched with a thorough numerical comparison on several classic problems, thus showing the strengths and weaknesses of each algorithm. A few selected applications, related to classification, denoising and compression, complete the view on the capabilities of the presented dictionary learning algorithms. The book isaccompanied by code for all algorithms and for reproducing most tables and figures.Presents all relevant dictionary learning algorithms - for the standard problem and its main variations - in detail and ready for implementation;Covers all dictionary structures that are meaningful in applications;Examines the numerical properties of the algorithms and shows how to choose the appropriate dictionary learning algorithm.…

Condition: New. In.

Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book covers all the relevant dictionary learning algorithms, presenting them in full detail and showing their distinct characteristics while also revealing the similarities. It gives implementation tricks that are often ignored but that are crucial for a successful program. Besides MOD, K-SVD, and other standard algorithms, it provides the significant dictionary learning problem variations, such as regularization, incoherence enforcing, finding an economical size, or learning adapted to specific problems like classification. Several types of dictionary structures are treated, including shift invariant; orthogonal blocks or factored dictionaries; and separable dictionaries for multidimensional signals. Nonlinear extensions such as kernel dictionary learning can also be found in the book. The discussion of all these dictionary types and algorithms is enriched with a thorough numerical comparison on several classic problems, thus showing the strengths and weaknesses of each algorithm. A few selected applications, related to classification, denoising and compression, complete the view on the capabilities of the presented dictionary learning algorithms. The book isaccompanied by code for all algorithms and for reproducing most tables and figures.Presents all relevant dictionary learning algorithms - for the standard problem and its main variations - in detail and ready for implementation;Covers all dictionary structures that are meaningful in applications;Examines the numerical properties of the algorithms and shows how to choose the appropriate dictionary learning algorithm.…

Condition: New. pp. XIV, 284 48 illus., 47 illus. in color. 1st ed. 2018 edition NO-PA16APR2015-KAP.

Paperback. Condition: New. NEW. SHIPS FROM MULTIPLE LOCATIONS. book.

Hardcover. Condition: New. NEW. SHIPS FROM MULTIPLE LOCATIONS. book.

Hardcover. Condition: Brand New. 284 pages. 9.25x6.25x0.75 inches. In Stock.

Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book covers all the relevant dictionary learning algorithms, presenting them in full detail and showing their distinct characteristics while also revealing the similarities. It gives implementation tricks that are often ignored but that are crucial for a successful program. Besides MOD, K-SVD, and other standard algorithms, it provides the significant dictionary learning problem variations, such as regularization, incoherence enforcing, finding an economical size, or learning adapted to specific problems like classification. Several types of dictionary structures are treated, including shift invariant; orthogonal blocks or factored dictionaries; and separable dictionaries for multidimensional signals. Nonlinear extensions such as kernel dictionary learning can also be found in the book. The discussion of all these dictionary types and algorithms is enriched with a thorough numerical comparison on several classic problems, thus showing the strengths and weaknesses of each algorithm. A few selected applications, related to classification, denoising and compression, complete the view on the capabilities of the presented dictionary learning algorithms. The book isaccompanied by code for all algorithms and for reproducing most tables and figures.Presents all relevant dictionary learning algorithms - for the standard problem and its main variations - in detail and ready for implementation;Covers all dictionary structures that are meaningful in applications;Examines the numerical properties of the algorithms and shows how to choose the appropriate dictionary learning algorithm. 300 pp. Englisch.…

Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Presents all relevant dictionary learning algorithms - for the standard problem and its main variations - in detail and ready for implementationCovers all dictionary structures that are meaningful in applicationsExamines the numerical pro.…

Condition: New. Print on Demand.

Condition: New. PRINT ON DEMAND.

Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book covers all the relevant dictionary learning algorithms, presenting them in full detail and showing their distinct characteristics while also revealing the similarities. It gives implementation tricks that are often ignored but that are crucial for a successful program. Besides MOD, K-SVD, and other standard algorithms, it provides the significant dictionary learning problem variations, such as regularization, incoherence enforcing, finding an economical size, or learning adapted to specific problems like classification. Several types of dictionary structures are treated, including shift invariant; orthogonal blocks or factored dictionaries; and separable dictionaries for multidimensional signals. Nonlinear extensions such as kernel dictionary learning can also be found in the book. The discussion of all these dictionary types and algorithms is enriched with a thorough numerical comparison on several classic problems, thus showing the strengths and weaknesses of each algorithm. A few selected applications, related to classification, denoising and compression, complete the view on the capabilities of the presented dictionary learning algorithms. The book isaccompanied by code for all algorithms and for reproducing most tables and figures.Presents all relevant dictionary learning algorithms - for the standard problem and its main variations - in detail and ready for implementation;Covers all dictionary structures that are meaningful in applications;Examines the numerical properties of the algorithms and shows how to choose the appropriate dictionary learning algorithm. 300 pp. Englisch.…

Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Presents all relevant dictionary learning algorithms - for the standard problem and its main variations - in detail and ready for implementationCovers all dictionary structures that are meaningful in applicationsExamines the numerical pro.…

Condition: New. Print on Demand pp. XIV, 284 48 illus., 47 illus. in color.

Condition: New. PRINT ON DEMAND pp. XIV, 284 48 illus., 47 illus. in color.