Trade Paperback. Zustand: Near Fine. *Excellent* Square, clean, and bright. No marks! A little wear to the exterior. I ship daily. 117 pp. Appendices. Bibliography.
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In den WarenkorbPaperback. Zustand: Brand New. 2015 edition. 100 pages. 9.30x6.20x0.23 inches. In Stock.
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In den WarenkorbZustand: Good. Your purchase helps support Sri Lankan Children's Charity 'The Rainbow Centre'. Ex-library, so some stamps and wear, and may have sticker on cover, but in good overall condition. Our donations to The Rainbow Centre have helped provide an education and a safe haven to hundreds of children who live in appalling conditions.
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Taschenbuch. Zustand: Neu. Data Analysis Applied To Satellite Images To Calculate The Deforestation | Pattern recognition applied to satellite images | Henry Solarte | Taschenbuch | Englisch | 2020 | Our Knowledge Publishing | EAN 9786203097443 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.
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Sprache: Englisch
Verlag: LAP LAMBERT Academic Publishing, 2010
ISBN 10: 3838394453 ISBN 13: 9783838394459
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Taschenbuch. Zustand: Neu. Pattern Recognition and the Whitehead Minimization Problem | Exploratory data analysis in abstract algebra: mathematical structure discovery through experiment. | Alexey Miasnikov | Taschenbuch | 152 S. | Englisch | 2010 | LAP LAMBERT Academic Publishing | EAN 9783838394459 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.
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Sprache: Englisch
Verlag: LAP LAMBERT Academic Publishing, 2011
ISBN 10: 3846548332 ISBN 13: 9783846548332
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Taschenbuch. Zustand: Neu. Learning Structured Data for Human Action Analysis | A unified approach for visual pattern recognition | Tuan Hue Thi | Taschenbuch | 228 S. | Englisch | 2011 | LAP LAMBERT Academic Publishing | EAN 9783846548332 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.
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In den WarenkorbZustand: New. pp. 256 97 Illus.
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Sprache: Englisch
Verlag: Lap Lambert Academic Publishing, 2011
ISBN 10: 3846548332 ISBN 13: 9783846548332
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In den WarenkorbPaperback. Zustand: Brand New. 228 pages. 8.66x5.91x0.52 inches. In Stock.
Sprache: Englisch
Verlag: Springer-Verlag New York Inc, 2013
ISBN 10: 331903409X ISBN 13: 9783319034096
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In den WarenkorbHardcover. Zustand: Brand New. 2014 edition. 245 pages. 9.75x6.75x0.75 inches. In Stock.
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Taschenbuch. Zustand: Neu. Data Analysis and Pattern Recognition in Multiple Databases | Animesh Adhikari (u. a.) | Taschenbuch | Intelligent Systems Reference Library | xv | Englisch | 2016 | Springer | EAN 9783319377278 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Sprache: Englisch
Verlag: Springer International Publishing, 2016
ISBN 10: 3319377272 ISBN 13: 9783319377278
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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Pattern recognition in data is a well known classical problem that falls under the ambit of data analysis. As we need to handle different data, the nature of patterns, their recognition and the types of data analyses are bound to change. Since the number of data collection channels increases in the recent time and becomes more diversified, many real-world data mining tasks can easily acquire multiple databases from various sources. In these cases, data mining becomes more challenging for several essential reasons. We may encounter sensitive data originating from different sources - those cannot be amalgamated. Even if we are allowed to place different data together, we are certainly not able to analyze them when local identities of patterns are required to be retained. Thus, pattern recognition in multiple databases gives rise to a suite of new, challenging problems different from those encountered before. Association rule mining, global pattern discovery and mining patterns of select items provide different patterns discovery techniques in multiple data sources. Some interesting item-based data analyses are also covered in this book. Interesting patterns, such as exceptional patterns, icebergs and periodic patterns have been recently reported. The book presents a thorough influence analysis between items in time-stamped databases. The recent research on mining multiple related databases is covered while some previous contributions to the area are highlighted and contrasted with the most recent developments.
Sprache: Englisch
Verlag: Springer International Publishing, 2013
ISBN 10: 331903409X ISBN 13: 9783319034096
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Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Pattern recognition in data is a well known classical problem that falls under the ambit of data analysis. As we need to handle different data, the nature of patterns, their recognition and the types of data analyses are bound to change. Since the number of data collection channels increases in the recent time and becomes more diversified, many real-world data mining tasks can easily acquire multiple databases from various sources. In these cases, data mining becomes more challenging for several essential reasons. We may encounter sensitive data originating from different sources - those cannot be amalgamated. Even if we are allowed to place different data together, we are certainly not able to analyze them when local identities of patterns are required to be retained. Thus, pattern recognition in multiple databases gives rise to a suite of new, challenging problems different from those encountered before. Association rule mining, global pattern discovery and mining patterns of select items provide different patterns discovery techniques in multiple data sources. Some interesting item-based data analyses are also covered in this book. Interesting patterns, such as exceptional patterns, icebergs and periodic patterns have been recently reported. The book presents a thorough influence analysis between items in time-stamped databases. The recent research on mining multiple related databases is covered while some previous contributions to the area are highlighted and contrasted with the most recent developments.
Sprache: Englisch
Verlag: Springer-Verlag New York Inc, 2016
ISBN 10: 3319377272 ISBN 13: 9783319377278
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In den WarenkorbPaperback. Zustand: Brand New. reprint edition. 256 pages. 9.25x6.10x0.58 inches. In Stock.
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Zustand: Sehr gut. Zustand: Sehr gut | Seiten: 256 | Sprache: Englisch | Produktart: Bücher | Pattern recognition in data is a well known classical problem that falls under the ambit of data analysis. As we need to handle different data, the nature of patterns, their recognition and the types of data analyses are bound to change. Since the number of data collection channels increases in the recent time and becomes more diversified, many real-world data mining tasks can easily acquire multiple databases from various sources. In these cases, data mining becomes more challenging for several essential reasons. We may encounter sensitive data originating from different sources - those cannot be amalgamated. Even if we are allowed to place different data together, we are certainly not able to analyze them when local identities of patterns are required to be retained. Thus, pattern recognition in multiple databases gives rise to a suite of new, challenging problems different from those encountered before. Association rule mining, global pattern discovery and mining patterns of select items provide different patterns discovery techniques in multiple data sources. Some interesting item-based data analyses are also covered in this book. Interesting patterns, such as exceptional patterns, icebergs and periodic patterns have been recently reported. The book presents a thorough influence analysis between items in time-stamped databases. The recent research on mining multiple related databases is covered while some previous contributions to the area are highlighted and contrasted with the most recent developments.
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Zustand: Sehr gut. Zustand: Sehr gut | Seiten: 256 | Sprache: Englisch | Produktart: Bücher | Pattern recognition in data is a well known classical problem that falls under the ambit of data analysis. As we need to handle different data, the nature of patterns, their recognition and the types of data analyses are bound to change. Since the number of data collection channels increases in the recent time and becomes more diversified, many real-world data mining tasks can easily acquire multiple databases from various sources. In these cases, data mining becomes more challenging for several essential reasons. We may encounter sensitive data originating from different sources - those cannot be amalgamated. Even if we are allowed to place different data together, we are certainly not able to analyze them when local identities of patterns are required to be retained. Thus, pattern recognition in multiple databases gives rise to a suite of new, challenging problems different from those encountered before. Association rule mining, global pattern discovery and mining patterns of select items provide different patterns discovery techniques in multiple data sources. Some interesting item-based data analyses are also covered in this book. Interesting patterns, such as exceptional patterns, icebergs and periodic patterns have been recently reported. The book presents a thorough influence analysis between items in time-stamped databases. The recent research on mining multiple related databases is covered while some previous contributions to the area are highlighted and contrasted with the most recent developments.
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In den WarenkorbZustand: New. In.
Sprache: Englisch
Verlag: Springer, Berlin|Springer Nature Singapore|Springer, 2023
ISBN 10: 9811915229 ISBN 13: 9789811915222
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Sprache: Englisch
Verlag: Springer, Berlin|Springer Nature Singapore|Springer, 2022
ISBN 10: 9811915199 ISBN 13: 9789811915192
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