Isbn: 9781447170556 - compression schemes for mining large datasets: a machine learning perspective (18 risultati)

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Condizione: New. pp. 213 Softcover reprint of the original 1st ed. 2013 edition NO-PA16APR2015-KAP.

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Paperback. Condizione: Brand New. reprint edition. 213 pages. 9.25x6.10x0.51 inches. In Stock.

Taschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book addresses the challenges of data abstraction generation using a least number of database scans, compressing data through novel lossy and non-lossy schemes, and carrying out clustering and classification directly in the compressed domain. Schemes are presented which are shown to be efficient both in terms of space and time, while simultaneously providing the same or better classification accuracy. Features: describes a non-lossy compression scheme based on run-length encoding of patterns with binary valued features; proposes a lossy compression scheme that recognizes a pattern as a sequence of features and identifying subsequences; examines whether the identification of prototypes and features can be achieved simultaneously through lossy compression and efficient clustering; discusses ways to make use of domain knowledge in generating abstraction; reviews optimal prototype selection using genetic algorithms; suggests possible ways of dealing with big data problems using multiagent systems.…

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Taschenbuch. Condizione: Neu. Compression Schemes for Mining Large Datasets | A Machine Learning Perspective | T. Ravindra Babu (u. a.) | Taschenbuch | Advances in Computer Vision and Pattern Recognition | xvi | Englisch | 2016 | Springer | EAN 9781447170556 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. …

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Condizione: New. Print on Demand pp. 213.

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book addresses the challenges of data abstraction generation using a least number of database scans, compressing data through novel lossy and non-lossy schemes, and carrying out clustering and classification directly in the compressed domain. Schemes are presented which are shown to be efficient both in terms of space and time, while simultaneously providing the same or better classification accuracy. Features: describes a non-lossy compression scheme based on run-length encoding of patterns with binary valued features; proposes a lossy compression scheme that recognizes a pattern as a sequence of features and identifying subsequences; examines whether the identification of prototypes and features can be achieved simultaneously through lossy compression and efficient clustering; discusses ways to make use of domain knowledge in generating abstraction; reviews optimal prototype selection using genetic algorithms; suggests possible ways of dealing with big data problems using multiagent systems. 216 pp. Englisch.…

Condizione: New. PRINT ON DEMAND pp. 213.

Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book addresses the challenges of data abstraction generation using a least number of database scans, compressing data through novel lossy and non-lossy schemes, and carrying out clustering and classification directly in the compressed domain. Schemes are presented which are shown to be efficient both in terms of space and time, while simultaneously providing the same or better classification accuracy. Features:describes a non-lossy compression scheme based on run-length encoding of patterns with binary valued features; proposes a lossy compression scheme that recognizes a pattern as a sequence of features and identifying subsequences; examines whether the identification of prototypes and features can be achieved simultaneously through lossy compression and efficient clustering; discusses ways to make use of domain knowledge in generating abstraction; reviews optimal prototype selection using genetic algorithms; suggests possible ways of dealing with big data problems using multiagent systems.Springer-Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 216 pp. Englisch.…