Publicado por Cambridge University Press, 2021
ISBN 10: 1108494315 ISBN 13: 9781108494311
Idioma: Inglés
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Publicado por Cambridge University Press, 2021
ISBN 10: 1108494315 ISBN 13: 9781108494311
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Publicado por Cambridge University Press, 2021
ISBN 10: 1108494315 ISBN 13: 9781108494311
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Publicado por Cambridge University Press, 2021
ISBN 10: 1108494315 ISBN 13: 9781108494311
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Publicado por Cambridge University Press, 2021
ISBN 10: 1108494315 ISBN 13: 9781108494311
Idioma: Inglés
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Publicado por Cambridge University Press, 2021
ISBN 10: 1108494315 ISBN 13: 9781108494311
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Publicado por Cambridge University Press, 2021
ISBN 10: 1108494315 ISBN 13: 9781108494311
Idioma: Inglés
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Publicado por Cambridge University Press, 2021
ISBN 10: 1108494315 ISBN 13: 9781108494311
Idioma: Inglés
Librería: Biblios, Frankfurt am main, HESSE, Alemania
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Publicado por Cambridge University Press, 2021
ISBN 10: 1108494315 ISBN 13: 9781108494311
Idioma: Inglés
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Añadir al carritoBuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - Understanding when and why algorithms work is a fundamental challenge. For problems ranging from clustering to linear programming to neural networks there are significant gaps between empirical performance and prediction based on traditional worst-case analysis. The book introduces exciting new methods for assessing algorithm performance.
Publicado por Cambridge University Press, Cambridge, 2021
ISBN 10: 1108494315 ISBN 13: 9781108494311
Idioma: Inglés
Librería: CitiRetail, Stevenage, Reino Unido
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Añadir al carritoHardcover. Condición: new. Hardcover. There are no silver bullets in algorithm design, and no single algorithmic idea is powerful and flexible enough to solve every computational problem. Nor are there silver bullets in algorithm analysis, as the most enlightening method for analyzing an algorithm often depends on the problem and the application. However, typical algorithms courses rely almost entirely on a single analysis framework, that of worst-case analysis, wherein an algorithm is assessed by its worst performance on any input of a given size. The purpose of this book is to popularize several alternatives to worst-case analysis and their most notable algorithmic applications, from clustering to linear programming to neural network training. Forty leading researchers have contributed introductions to different facets of this field, emphasizing the most important models and results, many of which can be taught in lectures to beginning graduate students in theoretical computer science and machine learning. Understanding when and why algorithms work is a fundamental challenge. For problems ranging from clustering to linear programming to neural networks there are significant gaps between empirical performance and prediction based on traditional worst-case analysis. The book introduces exciting new methods for assessing algorithm performance. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
Publicado por Cambridge University Press, 2021
ISBN 10: 1108494315 ISBN 13: 9781108494311
Idioma: Inglés
Librería: Revaluation Books, Exeter, Reino Unido
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Añadir al carritoHardcover. Condición: Brand New. 686 pages. 10.00x7.00x1.55 inches. In Stock.
Publicado por Cambridge University Press CUP, 2021
ISBN 10: 1108494315 ISBN 13: 9781108494311
Idioma: Inglés
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Publicado por Cambridge University Press, 2021
ISBN 10: 1108494315 ISBN 13: 9781108494311
Idioma: Inglés
Librería: Lucky's Textbooks, Dallas, TX, Estados Unidos de America
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Publicado por Cambridge University Press, Cambridge, 2021
ISBN 10: 1108494315 ISBN 13: 9781108494311
Idioma: Inglés
Librería: AussieBookSeller, Truganina, VIC, Australia
EUR 107,67
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Añadir al carritoHardcover. Condición: new. Hardcover. There are no silver bullets in algorithm design, and no single algorithmic idea is powerful and flexible enough to solve every computational problem. Nor are there silver bullets in algorithm analysis, as the most enlightening method for analyzing an algorithm often depends on the problem and the application. However, typical algorithms courses rely almost entirely on a single analysis framework, that of worst-case analysis, wherein an algorithm is assessed by its worst performance on any input of a given size. The purpose of this book is to popularize several alternatives to worst-case analysis and their most notable algorithmic applications, from clustering to linear programming to neural network training. Forty leading researchers have contributed introductions to different facets of this field, emphasizing the most important models and results, many of which can be taught in lectures to beginning graduate students in theoretical computer science and machine learning. Understanding when and why algorithms work is a fundamental challenge. For problems ranging from clustering to linear programming to neural networks there are significant gaps between empirical performance and prediction based on traditional worst-case analysis. The book introduces exciting new methods for assessing algorithm performance. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
Publicado por Cambridge University Press, Cambridge, 2021
ISBN 10: 1108494315 ISBN 13: 9781108494311
Idioma: Inglés
Librería: Grand Eagle Retail, Fairfield, OH, Estados Unidos de America
EUR 85,94
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Añadir al carritoHardcover. Condición: new. Hardcover. There are no silver bullets in algorithm design, and no single algorithmic idea is powerful and flexible enough to solve every computational problem. Nor are there silver bullets in algorithm analysis, as the most enlightening method for analyzing an algorithm often depends on the problem and the application. However, typical algorithms courses rely almost entirely on a single analysis framework, that of worst-case analysis, wherein an algorithm is assessed by its worst performance on any input of a given size. The purpose of this book is to popularize several alternatives to worst-case analysis and their most notable algorithmic applications, from clustering to linear programming to neural network training. Forty leading researchers have contributed introductions to different facets of this field, emphasizing the most important models and results, many of which can be taught in lectures to beginning graduate students in theoretical computer science and machine learning. Understanding when and why algorithms work is a fundamental challenge. For problems ranging from clustering to linear programming to neural networks there are significant gaps between empirical performance and prediction based on traditional worst-case analysis. The book introduces exciting new methods for assessing algorithm performance. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Publicado por Cambridge University Press, 2021
ISBN 10: 1108494315 ISBN 13: 9781108494311
Idioma: Inglés
Librería: Mispah books, Redhill, SURRE, Reino Unido
EUR 127,07
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Añadir al carritoHardcover. Condición: New. New. book.
Publicado por Cambridge University Press, 2021
ISBN 10: 1108494315 ISBN 13: 9781108494311
Idioma: Inglés
Librería: Revaluation Books, Exeter, Reino Unido
EUR 80,73
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Añadir al carritoHardcover. Condición: Brand New. 686 pages. 10.00x7.00x1.55 inches. In Stock. This item is printed on demand.
Publicado por Cambridge University Press, 2021
ISBN 10: 1108494315 ISBN 13: 9781108494311
Idioma: Inglés
Librería: moluna, Greven, Alemania
EUR 79,99
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Añadir al carritoCondición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Understanding when and why algorithms work is a fundamental challenge. For problems ranging from clustering to linear programming to neural networks there are significant gaps between empirical performance and prediction based on traditional worst-case anal.