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100%: Adam Schenker;Horst Bunke;Mark Last: Graph-theoretic Techniques For Web Content Mining (ISBN: 9789812569455) in Englisch, auch als eBook.Nur diese Ausgabe anzeigen…
56%: Schenker, Adam; Bunke, Horst; Last, Mark; Kandel, Abraham; Schenker, Dam: Graph-Theoretic Techniques for Web Content Mining (Machine Perception and Artificial Intelligence) (Series in Machine Perception and Artificial Intelligence) (ISBN: 9789812563392) 2005, World Scientific Publishing Co Pte Ltd, in Englisch, Broschiert.Nur diese Ausgabe anzeigen…
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Bester Preis: € 220,70 (vom 10.06.2020)1
Graph-Theoretic Techniques For Web Content Mining (2005)
EN HC NWISBN: 9789812563392 bzw. 9812563393, in Englisch, World Scientific Publishing Co Pte Ltd, gebundenes Buch, neu.
Lieferung aus: Niederlande, Vermoedelijk 4-6 weken.
bol.com.
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... 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 using multidimensional scaling.Soort: Met illustraties;Taal: Engels;Oorspronkelijke titel: Graph-Theoretic Techniques for Web Content Mining;Afmetingen: 0x0x0 mm;Gewicht: 494,00 gram;Verschijningsdatum: mei 2005;ISBN10: 9812563393;ISBN13: 9789812563392; Engelstalig | Hardcover | 2005.
bol.com.
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... 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 using multidimensional scaling.Soort: Met illustraties;Taal: Engels;Oorspronkelijke titel: Graph-Theoretic Techniques for Web Content Mining;Afmetingen: 0x0x0 mm;Gewicht: 494,00 gram;Verschijningsdatum: mei 2005;ISBN10: 9812563393;ISBN13: 9789812563392; Engelstalig | Hardcover | 2005.
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Graph-theoretic Techniques For Web Content Mining
~EN NW EB DLISBN: 9789812569455 bzw. 9812569456, vermutlich in Englisch, World Scientific Publishing Company, neu, E-Book, elektronischer Download.
Lieferung aus: Deutschland, Versandkostenfrei.
Graph-theoretic Techniques For Web Content Mining: 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 using multidimensional scaling. Englisch, Ebook.
Graph-theoretic Techniques For Web Content Mining: 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 using multidimensional scaling. Englisch, Ebook.
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Graph-Theoretic Techniques For Web Content Mining (Series in Machine Perception and Artificial Intelligence) (2005)
EN HC NWISBN: 9789812563392 bzw. 9812563393, in Englisch, 248 Seiten, World Scientific Publishing Co Pte Ltd, gebundenes Buch, neu.
Neu ab: £82.08 (13 Angebote)
Gebraucht ab: £56.29 (5 Angebote)
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Lieferung aus: Vereinigtes Königreich Großbritannien und Nordirland, Usually dispatched within 1-2 business days.
Von Händler/Antiquariat, Nearfine.
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 using multidimensional scaling. Hardcover, Label: World Scientific Publishing Co Pte Ltd, World Scientific Publishing Co Pte Ltd, Produktgruppe: Book, Publiziert: 2005-05-31, Studio: World Scientific Publishing Co Pte Ltd, Verkaufsrang: 6674631.
Von Händler/Antiquariat, Nearfine.
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 using multidimensional scaling. Hardcover, Label: World Scientific Publishing Co Pte Ltd, World Scientific Publishing Co Pte Ltd, Produktgruppe: Book, Publiziert: 2005-05-31, Studio: World Scientific Publishing Co Pte Ltd, Verkaufsrang: 6674631.
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Graph-Theoretic Techniques for Web Content Mining (Machine Perception and Artificial Intelligence) (Series in Machine Perception and Artificial Intelligence) (2005)
EN HC NWISBN: 9789812563392 bzw. 9812563393, in Englisch, World Scientific Pub Co Inc, gebundenes Buch, neu.
Lieferung aus: Vereinigte Staaten von Amerika, zzgl. Versandkosten, Verandgebiet: DOM.
Von Händler/Antiquariat, ExtremelyReliable, TX, Richmond, [RE:4].
Hardcover.
Von Händler/Antiquariat, ExtremelyReliable, TX, Richmond, [RE:4].
Hardcover.
6
Symbolbild
Graph-Theoretic Techniques for Web Content Mining (Machine Perception and Artificial Intelligence) (Series in Machine Perception and Artificial Intelligence) (2005)
EN HC USISBN: 9789812563392 bzw. 9812563393, in Englisch, World Scientific Pub Co Inc, gebundenes Buch, gebraucht.
Lieferung aus: Vereinigte Staaten von Amerika, zzgl. Versandkosten, Verandgebiet: DOM.
Von Händler/Antiquariat, ExtremelyReliable, TX, Richmond, [RE:4].
Hardcover.
Von Händler/Antiquariat, ExtremelyReliable, TX, Richmond, [RE:4].
Hardcover.
7
Symbolbild
Graph-Theoretic Techniques for Web Content Mining
EN HC NWISBN: 9789812563392 bzw. 9812563393, in Englisch, World Scientific Publishing Co Pte Ltd, gebundenes Buch, neu.
Lieferung aus: Vereinigtes Königreich Großbritannien und Nordirland, zzgl. Versandkosten, Verandgebiet: EUR.
Von Händler/Antiquariat, Paperbackshop International, GLOS, Fairford, [RE:4].
Hardcover.
Von Händler/Antiquariat, Paperbackshop International, GLOS, Fairford, [RE:4].
Hardcover.
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