Schenker adam (6 Ergebnisse)
- Hardcover
Anbieter: Universitätsbuchhandlung Herta Hold GmbH, Berlin, , DeutschlandUniversitätsbuchhandlung Herta Hold GmbH
Verkäufer/-in kontaktierenVerkäufer/-in mit 4 SternenZustand: Gebraucht
EUR 23,00
EUR 30,00 VersandVersand von Deutschland nach USAAnzahl: 1 verfügbar
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.
- Hardcover
Anbieter: Romtrade Corp., STERLING HEIGHTS, MI, USARomtrade Corp.
Verkäufer/-in kontaktierenVerkäufer/-in mit 5 SternenZustand: Neu
EUR 125,95
Versand nach gratisVersand innerhalb von USAAnzahl: 5 verfügbar
Zustand: 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 (Machine Perception and Artificial Intelligence) (Series in Machine Perception and Artificial Intelligence)
Schenker, Adam/ Bunke, Horst/ Last, Mark/ Kandel, Abraham/ Schenker, Dam
- Hardcover
Anbieter: Revaluation Books, Exeter, , Vereinigtes KönigreichRevaluation Books
Verkäufer/-in kontaktierenVerkäufer/-in mit 5 SternenZustand: Neu
EUR 169,31
EUR 11,56 VersandVersand von Vereinigtes Königreich nach USAAnzahl: 1 verfügbar
Hardcover. Zustand: Brand New. illustrated edition. 248 pages. 9.25x6.25x0.75 inches. In Stock.
- Hardcover
Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes KönigreichRia Christie Collections
Verkäufer/-in kontaktierenVerkäufer/-in mit 5 SternenZustand: Neu
EUR 194,99
EUR 13,85 VersandVersand von Vereinigtes Königreich nach USAAnzahl: 2 verfügbar
Zustand: New. In.
- Hardcover
Anbieter: moluna, Greven, , Deutschlandmoluna
Verkäufer/-in kontaktierenVerkäufer/-in mit 5 SternenZustand: Neu
EUR 167,87
EUR 48,99 VersandVersand von Deutschland nach USAAnzahl: 1 verfügbar
Gebunden. Zustand: 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.
Sprache: Englisch
Verlag: World Scientific Publishing Company Mai 2005 2005
- Hardcover
Anbieter: AHA-BUCH GmbH, Einbeck, DeutschlandAHA-BUCH GmbH
Verkäufer/-in kontaktierenVerkäufer/-in mit 5 SternenZustand: Neu
EUR 207,15
EUR 62,44 VersandVersand von Deutschland nach USAAnzahl: 1 verfügbar
Buch. Zustand: 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.
