EUR 150,62
Cantidad disponible: 15 disponibles
Añadir al carritoHRD. Condición: New. New Book. Shipped from UK. Established seller since 2000.
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
EUR 153,60
Cantidad disponible: Más de 20 disponibles
Añadir al carritoCondición: New.
Librería: PBShop.store US, Wood Dale, IL, Estados Unidos de America
EUR 155,92
Cantidad disponible: 15 disponibles
Añadir al carritoHRD. Condición: New. New Book. Shipped from UK. Established seller since 2000.
Librería: Ria Christie Collections, Uxbridge, Reino Unido
EUR 150,08
Cantidad disponible: Más de 20 disponibles
Añadir al carritoCondición: New. In.
EUR 150,07
Cantidad disponible: Más de 20 disponibles
Añadir al carritoCondición: New.
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
EUR 166,34
Cantidad disponible: Más de 20 disponibles
Añadir al carritoCondición: As New. Unread book in perfect condition.
EUR 167,30
Cantidad disponible: Más de 20 disponibles
Añadir al carritoCondición: As New. Unread book in perfect condition.
Librería: Majestic Books, Hounslow, Reino Unido
EUR 183,10
Cantidad disponible: 3 disponibles
Añadir al carritoCondición: New.
Librería: Revaluation Books, Exeter, Reino Unido
EUR 187,70
Cantidad disponible: 2 disponibles
Añadir al carritoHardcover. Condición: Brand New. 248 pages. 6.69x9.45x0.67 inches. In Stock.
Librería: Books Puddle, New York, NY, Estados Unidos de America
EUR 203,15
Cantidad disponible: 3 disponibles
Añadir al carritoCondición: New.
Idioma: Inglés
Publicado por Gruyter, Walter de GmbH, 2021
ISBN 10: 3110697092 ISBN 13: 9783110697094
Librería: Buchpark, Trebbin, Alemania
EUR 104,86
Cantidad disponible: 1 disponibles
Añadir al carritoCondición: Sehr gut. Zustand: Sehr gut | Sprache: Englisch | Produktart: Bücher | Keine Beschreibung verfügbar.
Librería: Rarewaves USA United, OSWEGO, IL, Estados Unidos de America
EUR 171,65
Cantidad disponible: Más de 20 disponibles
Añadir al carritoHardback. Condición: New. This book explains how to perform data de-noising, in large scale, with a satisfactory level of accuracy. Three main issues are considered. Firstly, how to eliminate the error propagation from one stage to next stages while developing a filtered model. Secondly, how to maintain the positional importance of data whilst purifying it. Finally, preservation of memory in the data is crucial to extract smart data from noisy big data. If, after the application of any form of smoothing or filtering, the memory of the corresponding data changes heavily, then the final data may lose some important information. This may lead to wrong or erroneous conclusions. But, when anticipating any loss of information due to smoothing or filtering, one cannot avoid the process of denoising as on the other hand any kind of analysis of big data in the presence of noise can be misleading. So, the entire process demands very careful execution with efficient and smart models in order to effectively deal with it.
Librería: Brook Bookstore On Demand, Napoli, NA, Italia
EUR 213,18
Cantidad disponible: Más de 20 disponibles
Añadir al carritoCondición: new.
EUR 159,95
Cantidad disponible: 2 disponibles
Añadir al carritoBuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book explains how to perform data de-noising, in large scale, with a satisfactory level of accuracy. Three main issues are considered. Firstly, how to eliminate the error propagation from one stage to next stages while developing a filtered model. Secondly, how to maintain the positional importance of data whilst purifying it. Finally, preservation of memory in the data is crucial to extract smart data from noisy big data. If, after the application of any form of smoothing or filtering, the memory of the corresponding data changes heavily, then the final data may lose some important information. This may lead to wrong or erroneous conclusions. But, when anticipating any loss of information due to smoothing or filtering, one cannot avoid the process of denoising as on the other hand any kind of analysis of big data in the presence of noise can be misleading. So, the entire process demands very careful execution with efficient and smart models in order to effectively deal with it.
Librería: moluna, Greven, Alemania
Original o primera edición
EUR 179,14
Cantidad disponible: Más de 20 disponibles
Añadir al carritoCondición: New. Souvik Bhattacharyya, Koushik Ghosh, University of Burdwan,West Bengal, India.This book explains how to perform data de-noising, in large scale, with a satisfactory level of accuracy. Three main issues are considered. Firstly,.
Librería: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Irlanda
EUR 232,22
Cantidad disponible: Más de 20 disponibles
Añadir al carritoCondición: New. 2022. Hardcover. . . . . .
Librería: Kennys Bookstore, Olney, MD, Estados Unidos de America
EUR 289,67
Cantidad disponible: Más de 20 disponibles
Añadir al carritoCondición: New. 2022. Hardcover. . . . . . Books ship from the US and Ireland.
Librería: Mispah books, Redhill, SURRE, Reino Unido
EUR 318,89
Cantidad disponible: 1 disponibles
Añadir al carritohardcover. Condición: New. NEW. SHIPS FROM MULTIPLE LOCATIONS. book.
Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
EUR 159,95
Cantidad disponible: 2 disponibles
Añadir al carritoBuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book explains how to perform data de-noising, in large scale, with a satisfactory level of accuracy. Three main issues are considered. Firstly, how to eliminate the error propagation from one stage to next stages while developing a filtered model. Secondly, how to maintain the positional importance of data whilst purifying it. Finally, preservation of memory in the data is crucial to extract smart data from noisy big data. If, after the application of any form of smoothing or filtering, the memory of the corresponding data changes heavily, then the final data may lose some important information. This may lead to wrong or erroneous conclusions. But, when anticipating any loss of information due to smoothing or filtering, one cannot avoid the process of denoising as on the other hand any kind of analysis of big data in the presence of noise can be misleading. So, the entire process demands very careful execution with efficient and smart models in order to effectively deal with it. 156 pp. Englisch.