Condizione: As New. Unread book in perfect condition.
Lingua: Inglese
Editore: Chapman and Hall/CRC, Boca Raton, 2014
ISBN 10: 1466586745 ISBN 13: 9781466586741
Da: Atlanta Vintage Books, Atlanta, GA, U.S.A.
Hardcover. Condizione: Fine. Condizione sovraccoperta: No DJ. Pages are clean, no markings from previous owners. Boards are clean. Binding is square and tight. Faint wear to cloth at spine ends and corners. Text block is clean. No dust jacket. PICTURES PROVIDED UPON REQUEST.
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Aggiungi al carrelloCondizione: New. pp. 707.
Condizione: New. pp. 707 1st edition NO-PA16APR2015-KAP.
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Da: GreatBookPricesUK, Woodford Green, Regno Unito
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Hardcover. Condizione: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority!
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Aggiungi al carrelloPaperback. Condizione: Brand New. 707 pages. 10.00x7.00x1.00 inches. In Stock.
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Robust Clustering Algorithms and Potential Applications | Algorithms for robust data clustering, image segmentation and data classification | Xu-Lei Yang | Taschenbuch | Englisch | VDM Verlag Dr. Müller | EAN 9783639180695 | 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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Aggiungi al carrelloTaschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Several novel and robust learning algorithms, with the aim to overcome the drawbacks of traditional clustering algorithms, are developed for data clustering and its applications. The effectiveness and superiority of the proposed methods are supported by experimental results. 1) Te proposed RDA exhibits several robust clustering characteristics: robust to the initialization; robust to cluster volumes; and robust to noise and outliers.2) The proposed IFCSS algorithm achieves two robust clustering characteristics: the robustness against noisy points is obtained by the maximization of mutual information; and the optimal cluster number is auto-determined by the VC-bound induced cluster validity.3) The KDA is developed to discover some complicated (e.g., linearly nonseparable) data structures which can not be revealed by traditional clustering methods in the standard Euclidean space.4) Finally, robust clustering methods have been developed for image segmentation and pattern classification. The proposed ASDA can perform unsupervised clustering for robust image segmentation. The KPCM is developed to generate weights used for SVM training.
Da: moluna, Greven, Germania
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Aggiungi al carrelloGebunden. Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Charu C. Aggarwal is a research scientist at the IBM T.J. Watson Research Center. A fellow of the IEEE and the ACM, he is the author/editor of ten books, an associate editor of several journals, and the vice-president of the SIAM Activit.
Da: preigu, Osnabrück, Germania
EUR 254,05
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Aggiungi al carrelloBuch. Condizione: Neu. Data Classification | Algorithms and Applications | Charu C. Aggarwal | Buch | Einband - fest (Hardcover) | Englisch | 2014 | Chapman and Hall/CRC | EAN 9781466586741 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 266,16
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Aggiungi al carrelloBuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Research on the problem of classification tends to be fragmented across such areas as pattern recognition, database, data mining, and machine learning. Addressing the work of these different communities in a unified way, this book explores the underlying algorithms of classification as well as applications of classification in a variety of problem domains, including text, multimedia, social network, and biological data. It presents core methods in data classification, covers recent problem domains, and discusses advanced methods for enhancing the quality of the underlying classification results.