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Graph classification and clustering based on vector space embedding

By: Contributor(s): Material type: TextTextSeries: Series in machine perception and aritificial intelligence; Vol.77Publication details: New Jersey World Scientific 2010 Description: xiv, 331 pISBN:
  • 9789814304719
Subject(s): DDC classification:
  • 516.65
Summary: his 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.
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Item type Current library Item location Shelving location Call number Status Date due Barcode
Books Vikram Sarabhai Library Rack 28-A / Slot 1386 (0 Floor, East Wing) General Stacks 516.65 R4G7 (Browse shelf(Opens below)) Available 170171

his 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.

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