9789812563392 - graph-theoretic techniques for web content mining: 62 di schenker, adam; bunke, horst; last, mark; kandel, abraham; schenker, dam (12 risultati)
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Hardcover. Condizione: 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.
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16 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.
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Condizione: Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service.
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Condizione: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.
Graph-theoretic Techniques for Web Content Mining
Schenker, Adam; Bunke, Horst; Last, Mark; Kandel, Abraham; Schenker, Dam
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Condizione: As New. Unread book in perfect condition.
Graph-Theoretic Techniques for Web Content Mining (Machine Perception and Artificial Intelligence) (Series in Machine Perception and Artificial Intelligence)
Schenker, Adam/ Bunke, Horst/ Last, Mark/ Kandel, Abraham/ Schenker, Dam
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Da: Revaluation Books, Exeter, , Regno UnitoRevaluation Books
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Hardcover. Condizione: Brand New. illustrated edition. 248 pages. 9.25x6.25x0.75 inches. In Stock.
Graph-theoretic Techniques for Web Content Mining
Schenker, Adam; Bunke, Horst; Last, Mark; Kandel, Abraham; Schenker, Dam
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Da: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
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Graph-theoretic Techniques for Web Content Mining
Schenker, Adam; Bunke, Horst; Last, Mark; Kandel, Abraham; Schenker, Dam
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Da: GreatBookPricesUK, Woodford Green, Regno UnitoGreatBookPricesUK
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Graph-theoretic Techniques for Web Content Mining
Schenker, Adam; Bunke, Horst; Last, Mark; Kandel, Abraham; Schenker, Dam
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Da: GreatBookPricesUK, Woodford Green, Regno UnitoGreatBookPricesUK
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Condizione: New.
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Condizione: New. In.
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Da: moluna, Greven, , Germaniamoluna
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Gebunden. Condizione: 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.
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Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH
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Buch. Condizione: 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.
