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Edité par Springer, 2001
ISBN 10 : 0792372093ISBN 13 : 9780792372097
Vendeur : Phatpocket Limited, Waltham Abbey, HERTS, Royaume-Uni
Livre
Etat : Like New. Used - Like New. Your purchase helps support Sri Lankan Children's Charity 'The Rainbow Centre'. Our donations to The Rainbow Centre have helped provide an education and a safe haven to hundreds of children who live in appalling conditions.
Edité par Kluwer Academic Publishers, 2001
ISBN 10 : 0792372093ISBN 13 : 9780792372097
Vendeur : BookOrders, Russell, IA, Etats-Unis
Livre
Hard Cover. Etat : Good. No Jacket. Usual ex-library features. The interior is clean and tight. Binding is good. Cover shows slight wear. 416 pages. Ex-Library.
Edité par Springer US, 2010
ISBN 10 : 1441948619ISBN 13 : 9781441948618
Vendeur : booksXpress, Bayonne, NJ, Etats-Unis
Livre
Soft Cover. Etat : new.
Edité par Springer, 2001
ISBN 10 : 0792372093ISBN 13 : 9780792372097
Vendeur : booksXpress, Bayonne, NJ, Etats-Unis
Livre
Hardcover. Etat : new.
Edité par Springer, 2001
ISBN 10 : 0792372093ISBN 13 : 9780792372097
Vendeur : Ria Christie Collections, Uxbridge, Royaume-Uni
Livre impression à la demande
Etat : New. PRINT ON DEMAND Book; New; Fast Shipping from the UK. No. book.
Edité par Springer, 2010
ISBN 10 : 1441948619ISBN 13 : 9781441948618
Vendeur : Ria Christie Collections, Uxbridge, Royaume-Uni
Livre impression à la demande
Etat : New. PRINT ON DEMAND Book; New; Fast Shipping from the UK. No. book.
Edité par Springer, 2010
ISBN 10 : 1441948619ISBN 13 : 9781441948618
Vendeur : Lucky's Textbooks, Dallas, TX, Etats-Unis
Livre
Etat : New.
Edité par Springer, 2001
ISBN 10 : 0792372093ISBN 13 : 9780792372097
Vendeur : Lucky's Textbooks, Dallas, TX, Etats-Unis
Livre
Etat : New.
Edité par Springer US Dez 2010, 2010
ISBN 10 : 1441948619ISBN 13 : 9781441948618
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
Livre impression à la demande
Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The ability to analyze and understand massive data sets lags far behind the ability to gather and store the data. To meet this challenge, knowledge discovery and data mining (KDD) is growing rapidly as an emerging field. However, no matter how powerful computers are now or will be in the future, KDD researchers and practitioners must consider how to manage ever-growing data which is, ironically, due to the extensive use of computers and ease of data collection with computers. Many different approaches have been used to address the data explosion issue, such as algorithm scale-up and data reduction. Instance, example, or tuple selection pertains to methods or algorithms that select or search for a representative portion of data that can fulfill a KDD task as if the whole data is used. Instance selection is directly related to data reduction and becomes increasingly important in many KDD applications due to the need for processing efficiency and/or storage efficiency. One of the major means of instance selection is sampling whereby a sample is selected for testing and analysis, and randomness is a key element in the process. Instance selection also covers methods that require search. Examples can be found in density estimation (finding the representative instances - data points - for a cluster); boundary hunting (finding the critical instances to form boundaries to differentiate data points of different classes); and data squashing (producing weighted new data with equivalent sufficient statistics). Other important issues related to instance selection extend to unwanted precision, focusing, concept drifts, noise/outlier removal, data smoothing, etc. Instance Selection and Construction for Data Mining brings researchers and practitioners together to report new developments and applications, to share hard-learned experiences in order to avoid similar pitfalls, and to shed light on the future development of instance selection. This volume serves as a comprehensive reference for graduate students, practitioners and researchers in KDD. 444 pp. Englisch.
Edité par Springer US Feb 2001, 2001
ISBN 10 : 0792372093ISBN 13 : 9780792372097
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
Livre impression à la demande
Buch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The ability to analyze and understand massive data sets lags far behind the ability to gather and store the data. To meet this challenge, knowledge discovery and data mining (KDD) is growing rapidly as an emerging field. However, no matter how powerful computers are now or will be in the future, KDD researchers and practitioners must consider how to manage ever-growing data which is, ironically, due to the extensive use of computers and ease of data collection with computers. Many different approaches have been used to address the data explosion issue, such as algorithm scale-up and data reduction. Instance, example, or tuple selection pertains to methods or algorithms that select or search for a representative portion of data that can fulfill a KDD task as if the whole data is used. Instance selection is directly related to data reduction and becomes increasingly important in many KDD applications due to the need for processing efficiency and/or storage efficiency. One of the major means of instance selection is sampling whereby a sample is selected for testing and analysis, and randomness is a key element in the process. Instance selection also covers methods that require search. Examples can be found in density estimation (finding the representative instances - data points - for a cluster); boundary hunting (finding the critical instances to form boundaries to differentiate data points of different classes); and data squashing (producing weighted new data with equivalent sufficient statistics). Other important issues related to instance selection extend to unwanted precision, focusing, concept drifts, noise/outlier removal, data smoothing, etc. Instance Selection and Construction for Data Mining brings researchers and practitioners together to report new developments and applications, to share hard-learned experiences in order to avoid similar pitfalls, and to shed light on the future development of instance selection. This volume serves as a comprehensive reference for graduate students, practitioners and researchers in KDD. 452 pp. Englisch.
Edité par Springer US, 2001
ISBN 10 : 0792372093ISBN 13 : 9780792372097
Vendeur : moluna, Greven, Allemagne
Livre impression à la demande
Gebunden. Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. The ability to analyze and understand massive data sets lags far behind the ability to gather and store the data. To meet this challenge, knowledge discovery and data mining (KDD) is growing rapidly as an emerging field. However, no matter how powerful .
Edité par Springer US, 2010
ISBN 10 : 1441948619ISBN 13 : 9781441948618
Vendeur : moluna, Greven, Allemagne
Livre impression à la demande
Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. The ability to analyze and understand massive data sets lags far behind the ability to gather and store the data. To meet this challenge, knowledge discovery and data mining (KDD) is growing rapidly as an emerging field. However, no matter how powerful .
Edité par Springer, 2001
ISBN 10 : 0792372093ISBN 13 : 9780792372097
Vendeur : Books Puddle, New York, NY, Etats-Unis
Livre
Etat : New. pp. 452.
Edité par Springer US, 2010
ISBN 10 : 1441948619ISBN 13 : 9781441948618
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
Livre
Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - The ability to analyze and understand massive data sets lags far behind the ability to gather and store the data. To meet this challenge, knowledge discovery and data mining (KDD) is growing rapidly as an emerging field. However, no matter how powerful computers are now or will be in the future, KDD researchers and practitioners must consider how to manage ever-growing data which is, ironically, due to the extensive use of computers and ease of data collection with computers. Many different approaches have been used to address the data explosion issue, such as algorithm scale-up and data reduction. Instance, example, or tuple selection pertains to methods or algorithms that select or search for a representative portion of data that can fulfill a KDD task as if the whole data is used. Instance selection is directly related to data reduction and becomes increasingly important in many KDD applications due to the need for processing efficiency and/or storage efficiency. One of the major means of instance selection is sampling whereby a sample is selected for testing and analysis, and randomness is a key element in the process. Instance selection also covers methods that require search. Examples can be found in density estimation (finding the representative instances - data points - for a cluster); boundary hunting (finding the critical instances to form boundaries to differentiate data points of different classes); and data squashing (producing weighted new data with equivalent sufficient statistics). Other important issues related to instance selection extend to unwanted precision, focusing, concept drifts, noise/outlier removal, data smoothing, etc. Instance Selection and Construction for Data Mining brings researchers and practitioners together to report new developments and applications, to share hard-learned experiences in order to avoid similar pitfalls, and to shed light on the future development of instance selection. This volume serves as a comprehensive reference for graduate students, practitioners and researchers in KDD.
Edité par Springer US, 2001
ISBN 10 : 0792372093ISBN 13 : 9780792372097
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
Livre
Buch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - The ability to analyze and understand massive data sets lags far behind the ability to gather and store the data. To meet this challenge, knowledge discovery and data mining (KDD) is growing rapidly as an emerging field. However, no matter how powerful computers are now or will be in the future, KDD researchers and practitioners must consider how to manage ever-growing data which is, ironically, due to the extensive use of computers and ease of data collection with computers. Many different approaches have been used to address the data explosion issue, such as algorithm scale-up and data reduction. Instance, example, or tuple selection pertains to methods or algorithms that select or search for a representative portion of data that can fulfill a KDD task as if the whole data is used. Instance selection is directly related to data reduction and becomes increasingly important in many KDD applications due to the need for processing efficiency and/or storage efficiency. One of the major means of instance selection is sampling whereby a sample is selected for testing and analysis, and randomness is a key element in the process. Instance selection also covers methods that require search. Examples can be found in density estimation (finding the representative instances - data points - for a cluster); boundary hunting (finding the critical instances to form boundaries to differentiate data points of different classes); and data squashing (producing weighted new data with equivalent sufficient statistics). Other important issues related to instance selection extend to unwanted precision, focusing, concept drifts, noise/outlier removal, data smoothing, etc. Instance Selection and Construction for Data Mining brings researchers and practitioners together to report new developments and applications, to share hard-learned experiences in order to avoid similar pitfalls, and to shed light on the future development of instance selection. This volume serves as a comprehensive reference for graduate students, practitioners and researchers in KDD.
Edité par Springer, 2001
ISBN 10 : 0792372093ISBN 13 : 9780792372097
Vendeur : Majestic Books, Hounslow, Royaume-Uni
Livre
Etat : New. pp. 452 52:B&W 6.14 x 9.21in or 234 x 156mm (Royal 8vo) Case Laminate on White w/Gloss Lam.
Edité par Springer, 2010
ISBN 10 : 1441948619ISBN 13 : 9781441948618
Vendeur : Mispah books, Redhill, SURRE, Royaume-Uni
Livre
Paperback. Etat : Like New. Like New. book.