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Archive entry:

## Graph-Theoretic Techniques For Web Content Mining (Series in Machine Perception and Artificial Intelligence) (2005) (?)

ISBN:

### 9789812563392 (?) or 9812563393

, in english, 248 pages, World Scientific Publishing Co Pte Ltd, hardcover, NewUsually dispatched within 1-2 business days

Neu ab: £82.08 (13 Angebote)

Gebraucht ab: £56.29 (5 Angebote)

Zu den weiteren 18 Angeboten bei Amazon.co.uk »

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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Keywords: Books, Computing & Internet, Computer Science, Artificial Intelligence, Databases, Data Storage & Management, Data Mining, Digital Lifestyle, Online Shopping, Amazon, Programming, Algorithms, Science & Nature, Mathematics, Combinatorics & Graph Theory, Education, Higher Education, Popular Science, Maths, Scientific, Technical & Medical

Data from 11/20/2015 14:28h

ISBN (alternative notations): 981-256-339-3, 978-981-256-339-2

Data from 11/20/2015 14:28h

ISBN (alternative notations): 981-256-339-3, 978-981-256-339-2

Neu ab: £82.08 (13 Angebote)

Gebraucht ab: £56.29 (5 Angebote)

Zu den weiteren 18 Angeboten bei Amazon.co.uk »

Archive entry:

## Graph-Theoretic Techniques for Web Content Mining (Machine Perception and Artificial Intelligence) (Series in Machine Perception and Artificial Intelligence) (2005) (?)

ISBN:

### 9789812563392 (?) or 9812563393

, unknown language, World Scientific Pub Co Inc, hardcover, Newplus shipping, Verandgebiet: DOM

hardcover

Data from 10/20/2016 13:43h

ISBN (alternative notations): 981-256-339-3, 978-981-256-339-2

ISBN (alternative notations): 981-256-339-3, 978-981-256-339-2

Archive entry:

## Graph-Theoretic Techniques for Web Content Mining (?)

ISBN:

### 9789812563392 (?) or 9812563393

, unknown language, World Scientific Publishing Co Pte Ltd, hardcover, Newplus shipping, Verandgebiet: EUR

hardcover

Data from 10/20/2016 13:43h

ISBN (alternative notations): 981-256-339-3, 978-981-256-339-2

ISBN (alternative notations): 981-256-339-3, 978-981-256-339-2

Archive entry:

## Graph-Theoretic Techniques for Web Content Mining (Machine Perception and Artificial Intelligence) (Series in Machine Perception and Artificial Intelligence) (2005) (?)

ISBN:

### 9789812563392 (?) or 9812563393

, unknown language, World Scientific Pub Co Inc, hardcover, Usedplus shipping, Verandgebiet: DOM

hardcover

Data from 10/20/2016 13:43h

ISBN (alternative notations): 981-256-339-3, 978-981-256-339-2

ISBN (alternative notations): 981-256-339-3, 978-981-256-339-2

Archive entry:

## Graph-Theoretic Techniques For Web Content Mining (2005) (?)

ISBN:

### 9789812563392 (?) or 9812563393

, unknown language, World Scientific Publishing Co Pte Ltd, hardcover, NewVermoedelijk 4-6 weken

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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Category: Database management, Data mining /, Techniek & Technologie, Informatica /, Meer computer, Kunstmatige intelligentie /, Internet, Online services /, Exacte wetenschappen, Wiskunde & Statistiek

Data from 10/20/2016 13:43h

ISBN (alternative notations): 981-256-339-3, 978-981-256-339-2

Data from 10/20/2016 13:43h

ISBN (alternative notations): 981-256-339-3, 978-981-256-339-2

### 9789812563392

Find all available books for your ISBN number 9789812563392 compare prices fast and easily and order immediatly.

Available rare books, used books and second hand books of the title "Graph-Theoretic Techniques for Web Content Mining (Machine Perception and Artificial Intelligence) (Series in Machine Perception and Artificial Intelligence)" from Schenker, Adam; Bunke, Horst; Last, Mark; Kandel, Abraham; Schenker, Dam are completely listed.

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