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Verlag: AV Akademikerverlag, 2013
ISBN 10: 3639474198ISBN 13: 9783639474190
Anbieter: Lucky's Textbooks, Dallas, TX, USA
Buch
Zustand: New.
Verlag: AV Akademikerverlag, 2013
ISBN 10: 3639474198ISBN 13: 9783639474190
Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes Königreich
Buch Print-on-Demand
Zustand: New. PRINT ON DEMAND Book; New; Fast Shipping from the UK. No. book.
Verlag: AV Akademikerverlag, 2013
ISBN 10: 3639474198ISBN 13: 9783639474190
Anbieter: PBShop.store US, Wood Dale, IL, USA
Buch Print-on-Demand
PAP. Zustand: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
Verlag: AV Akademikerverlag 2013-08, 2013
ISBN 10: 3639474198ISBN 13: 9783639474190
Anbieter: Chiron Media, Wallingford, Vereinigtes Königreich
Buch
PF. Zustand: New.
Verlag: AV Akademikerverlag Aug 2013, 2013
ISBN 10: 3639474198ISBN 13: 9783639474190
Anbieter: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Deutschland
Buch Print-on-Demand
Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -We approach the knapsack problem from a statistical learning perspective. We consider a stochastic setting with uncertainty about the description of the problem instances. As a consequence, uncertainty about the optimal solution arises. We present a characterization of different classes of knapsack problem instances based on their sensitivity to noise variations. We do so by calculating the informativeness as measured by the approximation set coding (ASC) principle. We also demonstrate experimentally that, depending on the problem instance class, the ability to reliably localize good knapsack solution sets may or may not be a requirement for good generalization performance. Furthermore, we present a parametrization of knapsack solutions based on the concept of a knapsack core. We show that this parametrization allows to regularize the model complexity of the knapsack learning problem. Algorithms based on the core concept may benefit from this parametrization to achieve better generalization performance at reduced running times. Finally, we consider a randomized approximation scheme for the counting knapsack problem proposed by Dyer. We employ the ASC principle to determine the maximally informative approximation ratio. 88 pp. Englisch.
Verlag: AV Akademikerverlag, 2013
ISBN 10: 3639474198ISBN 13: 9783639474190
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Buch Print-on-Demand
Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - We approach the knapsack problem from a statistical learning perspective. We consider a stochastic setting with uncertainty about the description of the problem instances. As a consequence, uncertainty about the optimal solution arises. We present a characterization of different classes of knapsack problem instances based on their sensitivity to noise variations. We do so by calculating the informativeness as measured by the approximation set coding (ASC) principle. We also demonstrate experimentally that, depending on the problem instance class, the ability to reliably localize good knapsack solution sets may or may not be a requirement for good generalization performance. Furthermore, we present a parametrization of knapsack solutions based on the concept of a knapsack core. We show that this parametrization allows to regularize the model complexity of the knapsack learning problem. Algorithms based on the core concept may benefit from this parametrization to achieve better generalization performance at reduced running times. Finally, we consider a randomized approximation scheme for the counting knapsack problem proposed by Dyer. We employ the ASC principle to determine the maximally informative approximation ratio.
Verlag: AV Akademikerverlag, 2013
ISBN 10: 3639474198ISBN 13: 9783639474190
Anbieter: PBShop.store UK, Fairford, GLOS, Vereinigtes Königreich
Buch Print-on-Demand
PAP. Zustand: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
Verlag: AV Akademikerverlag, 2013
ISBN 10: 3639474198ISBN 13: 9783639474190
Anbieter: moluna, Greven, Deutschland
Buch Print-on-Demand
Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Stelling Simonwas 25 years old when he attained his master s degree in computer science at ETH Zuerich. He currently works as a software engineer at Ergon Informatik.We approach the knapsack problem from a statistical learning per.