Graph Classification and Clustering Based on Vector Space Embedding (Series in Machine Perception and Artificial Intelligence) (Series in Machine Perception and Artifical Intelligence)

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9789814304719: Graph Classification and Clustering Based on Vector Space Embedding (Series in Machine Perception and Artificial Intelligence) (Series in Machine Perception and Artifical Intelligence)
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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.

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It is recommended for the data mining community working on graphs. --Mathematical Reviews

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Book Description World Scientific Publishing Co Pte Ltd, Singapore, 2010. Hardback. Condition: New. Language: English . Brand New Book. 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. Seller Inventory # AAC9789814304719

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Book Description World Scientific Publishing Co Pte Ltd, Singapore, 2010. Hardback. Condition: New. Language: English . Brand New Book. 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. Seller Inventory # AAC9789814304719

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Book Description World Scientific Publishing Co Pte Ltd. Hardback. Condition: new. BRAND NEW, Graph Classification and Clustering Based on Vector Space Embedding, Kaspar Riesen, Horst Bunke, 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. Seller Inventory # B9789814304719

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Book Description World Scientific Publishing Company. Hardcover. Condition: New. 348 pages. Dimensions: 9.0in. x 6.1in. x 1.0in.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. This item ships from multiple locations. Your book may arrive from Roseburg,OR, La Vergne,TN. Hardcover. Seller Inventory # 9789814304719

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