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Structural, Syntactic, and Statistical Pattern Recognition [electronic resource] : Joint IAPR International Workshop, S+SSPR 2016, M�erida, Mexico, November 29 - December 2, 2016, Proceedings / edited by Antonio Robles-Kelly, Marco Loog, Battista Biggio, Francisco Escolano, Richard Wilson.

Contributor(s): Robles-Kelly, Antonio [editor.] | Loog, Marco [editor.] | Biggio, Battista [editor.] | Escolano, Francisco [editor.] | Wilson, Richard [editor.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Lecture Notes in Computer Science: 10029Publisher: Cham : Springer International Publishing : Imprint: Springer, 2016Description: XIII, 588 p. 167 illus. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783319490557.Subject(s): Computer science | Algorithms | Database management | Data mining | Artificial intelligence | Pattern recognition | Computer Science | Artificial Intelligence (incl. Robotics) | Pattern Recognition | Information Systems Applications (incl. Internet) | Database Management | Algorithm Analysis and Problem Complexity | Data Mining and Knowledge DiscoveryAdditional physical formats: Printed edition:: No titleDDC classification: 006.3 Online resources: Click here to access online
Contents:
Dimensionality reduction -- Manifold learning and embedding methods.-Dissimilarity representations -- Graph-theoretic methods -- Model selection, classification and clustering -- Semi and fully supervised learning methods -- Shape analysis -- Spatio-temporal pattern recognition -- Structural matching -- Text and document analysis. .
In: Springer eBooksSummary: This book constitutes the proceedings of the Joint IAPR International Workshop on Structural Syntactic, and Statistical Pattern Recognition, S+SSPR 2016, consisting of the International Workshop on Structural and Syntactic Pattern Recognition SSPR, and the International Workshop on Statistical Techniques in Pattern Recognition, SPR. The 51 full papers presented were carefully reviewed and selected from 68 submissions. They are organized in the following topical sections: dimensionality reduction, manifold learning and embedding methods; dissimilarity representations; graph-theoretic methods; model selection, classification and clustering; semi and fully supervised learning methods; shape analysis; spatio-temporal pattern recognition; structural matching; text and document analysis. .
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Dimensionality reduction -- Manifold learning and embedding methods.-Dissimilarity representations -- Graph-theoretic methods -- Model selection, classification and clustering -- Semi and fully supervised learning methods -- Shape analysis -- Spatio-temporal pattern recognition -- Structural matching -- Text and document analysis. .

This book constitutes the proceedings of the Joint IAPR International Workshop on Structural Syntactic, and Statistical Pattern Recognition, S+SSPR 2016, consisting of the International Workshop on Structural and Syntactic Pattern Recognition SSPR, and the International Workshop on Statistical Techniques in Pattern Recognition, SPR. The 51 full papers presented were carefully reviewed and selected from 68 submissions. They are organized in the following topical sections: dimensionality reduction, manifold learning and embedding methods; dissimilarity representations; graph-theoretic methods; model selection, classification and clustering; semi and fully supervised learning methods; shape analysis; spatio-temporal pattern recognition; structural matching; text and document analysis. .

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