Instance selection construction data (21 resultados)
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Hard Cover. Condición: Good. No Jacket. Usual ex-library features. The interior is clean and tight. Binding is good. Cover shows slight wear. 416 pages. Ex-Library.
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Editorial: Springer, 2010
Serie: Libro 225 de 260 - The Springer International Series in Engineering and Computer Science
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Taschenbuch. Condición: Neu. Instance Selection and Construction for Data Mining | Huan Liu (u. a.) | Taschenbuch | xxv | Englisch | 2010 | Springer | EAN 9781441948618 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Idioma: Inglés
Editorial: Kluwer Academic Publishers, 2001
Serie: Libro 225 de 260 - The Springer International Series in Engineering and Computer Science
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Condición: New. 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 as an emerging field. This volume brings researchers and practitioners together to report developments and focuses on the d…evelopment of instance selection. Editor(s): Liu, Huan; Motoda, H. Series: The Springer International Series in Engineering and Computer Science. Num Pages: 416 pages, biography. BIC Classification: UN; UYQ. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly; (UU) Undergraduate. Dimension: 234 x 156 x 25. Weight in Grams: 807. . 2001. Hardback. . . . .
Idioma: Inglés
Editorial: Springer, Springer, 2010
Serie: Libro 225 de 260 - The Springer International Series in Engineering and Computer Science
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Taschenbuch. Condición: 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 powe…rful 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.
Idioma: Inglés
Editorial: Springer, Springer, 2001
Serie: Libro 225 de 260 - The Springer International Series in Engineering and Computer Science
- Tapa dura
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Buch. Condición: 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 co…mputers 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.
Idioma: Inglés
Editorial: Springer, 2010
Serie: Libro 225 de 260 - The Springer International Series in Engineering and Computer Science
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Paperback. Condición: Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.
Idioma: Inglés
Editorial: Kluwer Academic Publishers, 2001
Serie: Libro 225 de 260 - The Springer International Series in Engineering and Computer Science
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Condición: New. 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 as an emerging field. This volume brings researchers and practitioners together to report developments and focuses on the d…evelopment of instance selection. Editor(s): Liu, Huan; Motoda, H. Series: The Springer International Series in Engineering and Computer Science. Num Pages: 416 pages, biography. BIC Classification: UN; UYQ. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly; (UU) Undergraduate. Dimension: 234 x 156 x 25. Weight in Grams: 807. . 2001. Hardback. . . . . Books ship from the US and Ireland.
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Buch. Condición: 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.
Idioma: Inglés
Editorial: Springer US Dez 2010, 2010
Serie: Libro 225 de 260 - The Springer International Series in Engineering and Computer Science
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Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, AlemaniaBuchWeltWeit Ludwig Meier e.K.
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Taschenbuch. Condición: 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.
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Serie: Libro 225 de 260 - The Springer International Series in Engineering and Computer Science
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Condición: 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 fie…ld. However, no matter how powerful .
Idioma: Inglés
Editorial: Springer US, 2001
Serie: Libro 225 de 260 - The Springer International Series in Engineering and Computer Science
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- Impresión bajo demanda
Librería: moluna, Greven, Alemaniamoluna
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Gebunden. Condición: 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 em…erging field. However, no matter how powerful .
Más imágenesIdioma: Inglés
Editorial: Springer, 2001
Serie: Libro 225 de 260 - The Springer International Series in Engineering and Computer Science
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Buch. Condición: Neu. Instance Selection and Construction for Data Mining | Huan Liu (u. a.) | Buch | xxv | Englisch | 2001 | Springer | EAN 9780792372097 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on Demand.
Idioma: Inglés
Editorial: Springer, Springer Feb 2001, 2001
Serie: Libro 225 de 260 - The Springer International Series in Engineering and Computer Science
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Buch. Condición: Neu. This item is printed on demand - Print on Demand Titel. 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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 452 pp. Englisch.
Idioma: Inglés
Editorial: Springer, Springer Dez 2010, 2010
Serie: Libro 225 de 260 - The Springer International Series in Engineering and Computer Science
- Tapa blanda
- Impresión bajo demanda
Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemaniabuchversandmimpf2000
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EUR 160,49
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Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. 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 mat…ter 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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 444 pp. Englisch.
