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Graph Theoretic Techniques Web Content by Schenker Adam (10 results)
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Graph-Theoretic Techniques for Web Content Mining
Language: English
Published by World Scientific Pub Co Inc, 2005
ISBN 10: 9812563393 ISBN 13: 9789812563392
Seller: Isaiah Thomas Books & Prints, Inc., Cotuit, MA, U.S.A.
Association Member: ABAA ILAB SNEAB
Seller rating 2 out of 5 stars
Hardcover. Condition: 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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Graph-Theoretic Techniques for Web Content Mining (Machine Perception and Artificial Intelligence).
Language: English
Published by World Scientific Publishing Company., 2005
ISBN 10: 9812563393 ISBN 13: 9789812563392
Seller: Universitätsbuchhandlung Herta Hold GmbH, Berlin, Germany
Association Member: GIAQ ILAB VDA
Seller rating 5 out of 5 stars
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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Graph-theoretic Techniques for Web Content Mining
Language: English
Published by World Scientific Publishing Company, 2005
ISBN 10: 9812563393 ISBN 13: 9789812563392
Condition: As New. Unread book in perfect condition.
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Graph-Theoretic Techniques for Web Content Mining (Machine Perception and Artificial Intelligence) (Series in Machine Perception and Artificial Intelligence)
Language: English
Published by World Scientific Pub Co Inc, 2005
ISBN 10: 9812563393 ISBN 13: 9789812563392
Hardcover. Condition: Brand New. illustrated edition. 248 pages. 9.25x6.25x0.75 inches. In Stock.
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Graph-theoretic Techniques for Web Content Mining
Language: English
Published by World Scientific Publishing Company, 2005
ISBN 10: 9812563393 ISBN 13: 9789812563392
Condition: New.
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Graph-theoretic Techniques for Web Content Mining
Language: English
Published by World Scientific Publishing Company, 2005
ISBN 10: 9812563393 ISBN 13: 9789812563392
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: As New. Unread book in perfect condition.
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Graph-theoretic Techniques for Web Content Mining
Language: English
Published by World Scientific Publishing Company, 2005
ISBN 10: 9812563393 ISBN 13: 9789812563392
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: New.
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GRAPH-THEORETIC TECHNIQUES FOR WEB CONTENT MINING (Machine Perception and Artificial Intelligence)
Language: English
Published by World Scientific Publishing Company, 2005
ISBN 10: 9812563393 ISBN 13: 9789812563392
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
Condition: New. In.
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Graph-theoretic Techniques For Web Content Mining
Language: English
Published by WORLD SCIENTIFIC PUB CO INC, 2005
ISBN 10: 9812563393 ISBN 13: 9789812563392
Gebunden. Condition: 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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Graph-Theoretic Techniques for Web Content Mining
Language: English
Published by World Scientific Publishing Company Mai 2005, 2005
ISBN 10: 9812563393 ISBN 13: 9789812563392
Buch. Condition: 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.
