Graph classification and clustering based on vector space embedding /

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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...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Riesen, Kaspar
Otros Autores: Bunke, Horst
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Singapore ; London : World Scientific, ©2010.
Colección:Series in machine perception and artificial intelligence ; v. 77.
Temas:
Optical pattern recognition.
Artificial intelligence > Graphic methods.
Vector spaces.
Reconnaissance optique des formes (Informatique)
Intelligence artificielle > Méthodes graphiques.
Espaces vectoriels.
COMPUTERS > Optical Data Processing.
COMPUTERS > Computer Vision & Pattern Recognition.
Optical pattern recognition
Vector spaces
Acceso en línea:Texto completo
Tabla de Contenidos:
  • 1. Introduction and basic concepts. 1.1. Pattern recognition. 1.2. Learning methodology. 1.3. Statistical and structural pattern recognition. 1.4. Dissimilarity representation for pattern recognition. 1.5. Summary and outline
  • 2. Graph matching. 2.1. Graph and subgraph. 2.2. Exact graph matching. 2.3. Error-tolerant graph matching. 2.4. Summary and broader perspective
  • 3. Graph edit distance. 3.1. Basic definition and properties. 3.2. Exact computation of GED. 3.3. Efficient approximation algorithms. 3.4. Exact vs. approximate graph edit distance
  • an experimental evaluation. 3.5. Summary
  • 4. Graph data. 4.1. Graph data sets. 4.2. Evaluation of graph edit distance. 4.3. Data visualization. 4.4. Summary
  • 5. Kernel methods. 5.1. Overview and primer on kernel theory. 5.2. Kernel functions. 5.3. Feature map vs. kernel trick. 5.4. Kernel machines. 5.5. Graph kernels. 5.6. Experimental evaluation. 5.7. Summary
  • 6. Graph embedding using dissimilarities. 6.1. Related work. 6.2. Graph embedding using dissimilarities. 6.3. Prototype selection strategies. 6.4. Prototype reduction schemes. 6.5. Feature selection algorithms. 6.6. Defining the reference sets for Lipschitz embeddings. 6.7. Ensemble methods. 6.8. Summary
  • 7. Classification experiments with vector space embedded graphs. 7.1. Nearest-neighbor classifiers applied to vector space embedded graphs. 7.2. Support vector machines applied to vector space embedded graphs. 7.3. Summary and discussion
  • 8. Clustering experiments with vector space embedded graphs. 8.1. Experimental setup and validation of the meta parameters. 8.2. Results and discussion. 8.3. Summary and discussion
  • 9. Conclusions.

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