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Published by AV Akademikerverlag, 2013
ISBN 10: 3639474198ISBN 13: 9783639474190
Seller: Lucky's Textbooks, Dallas, TX, U.S.A.
Book
Condition: New.
Published by AV Akademikerverlag, 2013
ISBN 10: 3639474198ISBN 13: 9783639474190
Seller: Ria Christie Collections, Uxbridge, United Kingdom
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Condition: New. PRINT ON DEMAND Book; New; Fast Shipping from the UK. No. book.
Published by AV Akademikerverlag, 2013
ISBN 10: 3639474198ISBN 13: 9783639474190
Seller: PBShop.store US, Wood Dale, IL, U.S.A.
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PAP. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
Published by AV Akademikerverlag 2013-08, 2013
ISBN 10: 3639474198ISBN 13: 9783639474190
Seller: Chiron Media, Wallingford, United Kingdom
Book
PF. Condition: New.
Published by AV Akademikerverlag Aug 2013, 2013
ISBN 10: 3639474198ISBN 13: 9783639474190
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
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Taschenbuch. Condition: 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.
Published by AV Akademikerverlag, 2013
ISBN 10: 3639474198ISBN 13: 9783639474190
Seller: PBShop.store UK, Fairford, GLOS, United Kingdom
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PAP. Condition: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
Published by AV Akademikerverlag, 2013
ISBN 10: 3639474198ISBN 13: 9783639474190
Seller: AHA-BUCH GmbH, Einbeck, Germany
Book Print on Demand
Taschenbuch. Condition: 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.
Published by AV Akademikerverlag, 2013
ISBN 10: 3639474198ISBN 13: 9783639474190
Seller: moluna, Greven, Germany
Book Print on Demand
Condition: 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.