Publicado por World Scientific Publishing Co Pte Ltd, 2005
ISBN 10: 9812563393 ISBN 13: 9789812563392
Idioma: Inglés
Librería: Ammareal, Morangis, Francia
Cantidad disponible: 1 disponibles
Añadir al carritoHardcover. Condición: Très bon. Ancien livre de bibliothèque. Edition 2005. Ammareal reverse jusqu'à 15% du prix net de cet article à des organisations caritatives. ENGLISH DESCRIPTION Book Condition: Used, Very good. Former library book. Edition 2005. Ammareal gives back up to 15% of this item's net price to charity organizations.
Publicado por World Scientific Publishing Company., 2005
ISBN 10: 9812563393 ISBN 13: 9789812563392
Idioma: Inglés
Librería: Universitätsbuchhandlung Herta Hold GmbH, Berlin, Alemania
EUR 23,00
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Añadir al carrito16 x 23 cm. 248 pages. Hardcover. Versand aus Deutschland / We dispatch from Germany via Air Mail. Einband bestoßen, daher Mängelexemplar gestempelt, sonst sehr guter Zustand. Imperfect copy due to slightly bumped cover, apart from this in very good condition. Stamped. Sprache: Englisch.
Publicado por World Scientific Pub Co Inc, 2005
ISBN 10: 9812563393 ISBN 13: 9789812563392
Idioma: Inglés
Librería: Isaiah Thomas Books & Prints, Inc., Cotuit, MA, Estados Unidos de America
EUR 30,65
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Añadir al carritoHardcover. Condición: Fine. Fine new copy in dj. Review slip from publisher laid in. sci; Series In Machine Perception And Artificial Intelligence; 9.0 X 6.2 X 0.9 inches; 235 pages.
Publicado por World Scientific Pub Co Inc, 2005
ISBN 10: 9812563393 ISBN 13: 9789812563392
Idioma: Inglés
Librería: Revaluation Books, Exeter, Reino Unido
EUR 169,30
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Añadir al carritoHardcover. Condición: Brand New. illustrated edition. 248 pages. 9.25x6.25x0.75 inches. In Stock.
Publicado por World Scientific Publishing Company, 2005
ISBN 10: 9812563393 ISBN 13: 9789812563392
Idioma: Inglés
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
EUR 167,52
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Añadir al carritoCondición: As New. Unread book in perfect condition.
Publicado por World Scientific Publishing Company, 2005
ISBN 10: 9812563393 ISBN 13: 9789812563392
Idioma: Inglés
Librería: GreatBookPricesUK, Woodford Green, Reino Unido
EUR 168,90
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Añadir al carritoCondición: As New. Unread book in perfect condition.
Publicado por WORLD SCIENTIFIC PUB CO INC, 2005
ISBN 10: 9812563393 ISBN 13: 9789812563392
Idioma: Inglés
Librería: moluna, Greven, Alemania
EUR 164,38
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Añadir al carritoGebunden. Condición: New. Describes opportunities for utilizing robust graph representations of data with machine learning algorithms. The authors have selected the domain of web content mining, which involves the clustering and classification of web documents based on their textual.
Publicado por World Scientific Publishing Company, 2005
ISBN 10: 9812563393 ISBN 13: 9789812563392
Idioma: Inglés
Librería: GreatBookPricesUK, Woodford Green, Reino Unido
EUR 177,62
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Añadir al carritoCondición: New.
Publicado por World Scientific Publishing Company, 2005
ISBN 10: 9812563393 ISBN 13: 9789812563392
Idioma: Inglés
Librería: Ria Christie Collections, Uxbridge, Reino Unido
EUR 197,09
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Añadir al carritoCondición: New. In.
Publicado por World Scientific Publishing Company, 2005
ISBN 10: 9812563393 ISBN 13: 9789812563392
Idioma: Inglés
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
EUR 201,84
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Añadir al carritoCondición: New.
Publicado por World Scientific Publishing Company Mai 2005, 2005
ISBN 10: 9812563393 ISBN 13: 9789812563392
Idioma: Inglés
Librería: AHA-BUCH GmbH, Einbeck, Alemania
EUR 202,86
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Añadir al carritoBuch. Condición: Neu. Neuware - This book describes exciting new opportunities for utilizing robust graph representations of data with common machine learning algorithms. Graphs can model additional information which is often not present in commonly used data representations, such as vectors. Through the use of graph distance -- a relatively new approach for determining graph similarity -- the authors show how well-known algorithms, such as k-means clustering and k-nearest neighbors classification, can be easily extended to work with graphs instead of vectors. This allows for the utilization of additional information found in graph representations, while at the same time employing well-known, proven algorithms. To demonstrate and investigate these novel techniques, the authors have selected the domain of web content mining, which involves the clustering and classification of web documents based on their textual substance. Several methods of representing web document content by graphs are introduced; an interesting feature of these representations is that they allow for a polynomial time distance computation, something which is typically an NP-complete problem when using graphs. Experimental results are reported for both clustering and classification in three web document collections using a variety of graph representations, distance measures, and algorithm parameters. In addition, this book describes several other related topics, many of which provide excellent starting points for researchers and students interested in exploring this new area of machine learning further. These topics include creating graph-based multiple classifier ensembles through random node selection and visualization of graph-based data usingmultidimensional scaling.