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Graph classification and clustering ...

Bunke, Horst.

Graph classification and clustering based on vector space embedding

Record Type:	Electronic resources : Monograph/item
Title/Author:	Graph classification and clustering based on vector space embeddingKaspar Riesen & Horst Bunke.
Author:	Riesen, Kaspar.
other author:	Bunke, Horst.
Published:	Singapore ;World Scientific Pub. Co.,c2010.
Description:	xiv, 331 p. :ill.
Subject:	Optical pattern recognition.
Online resource:	http://www.worldscientific.com/worldscibooks/10.1142/7731#t=toc
ISBN:	9789814304726 (electronic bk.)

Graph classification and clustering based on vector space embedding
Riesen, Kaspar.

Graph classification and clustering based on vector space embedding[electronic resource] /Kaspar Riesen & Horst Bunke. - Singapore ;World Scientific Pub. Co.,c2010. - xiv, 331 p. :ill. - Series in machine perception and artificial intelligence ;v. 77. - Series in machine perception and artificial intelligence ;v. 74..

Includes bibliographical references (p. 309-328) and index.

This book is concerned with a fundamentally novel approach to graph-based pattern recognition based on vector space embedding of graphs. It aims at condensing the high representational power of graphs into a computationally efficient and mathematically convenient feature vector. This volume utilizes the dissimilarity space representation originally proposed by Duin and Pekalska to embed graphs in real vector spaces. Such an embedding gives one access to all algorithms developed in the past for feature vectors, which has been the predominant representation formalism in pattern recognition and related areas for a long time.

Electronic reproduction.
Singapore :
World Scientific Publishing Co.,
2010.

System requirements: Adobe Acrobat Reader.

ISBN: 9789814304726 (electronic bk.)Subjects--Topical Terms:

188326
Optical pattern recognition.

LC Class. No.: TA1650

Dewey Class. No.: 006.42

Graph classification and clustering based on vector space embedding
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245 1 0 $a Graph classification and clustering based on vector space embedding $h [electronic resource] / $c Kaspar Riesen & Horst Bunke.
260 $a Singapore ; $a Hackensack, N.J. : $b World Scientific Pub. Co., $c c2010.
300 $a xiv, 331 p. : $b ill.
490 1 $a Series in machine perception and artificial intelligence ; $v v. 77
504 $a Includes bibliographical references (p. 309-328) and index.
520 $a This book is concerned with a fundamentally novel approach to graph-based pattern recognition based on vector space embedding of graphs. It aims at condensing the high representational power of graphs into a computationally efficient and mathematically convenient feature vector. This volume utilizes the dissimilarity space representation originally proposed by Duin and Pekalska to embed graphs in real vector spaces. Such an embedding gives one access to all algorithms developed in the past for feature vectors, which has been the predominant representation formalism in pattern recognition and related areas for a long time.
533 $a Electronic reproduction. $b Singapore : $c World Scientific Publishing Co., $d 2010. $n System requirements: Adobe Acrobat Reader. $n Mode of access: World Wide Web. $n Available to subscribing institutions.
650 0 $a Optical pattern recognition. $3 188326
650 0 $a Artificial intelligence $x Graphic methods. $3 575646
650 0 $a Vector spaces. $3 210526
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