Structural, Syntactic, and Statistical Pattern Recognition : Joint IAPR International Workshop, S+SSPR 2016, Mérida, Mexico, November 29 - December 2, 2016, Proceedings

Antonio Robles-Kelly

ISBN 10: 3319490540 / ISBN 13: 9783319490540

Verlag: Springer International Publishing, 2016

Beschreibung

Druck auf Anfrage Neuware - Printed after ordering - This book constitutes the proceedings of the Joint IAPR International Workshop on StructuralSyntactic, 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. Bestandsnummer des Verkäufers 9783319490540

Bibliografische Details

Titel: Structural, Syntactic, and Statistical ...

Verlag: Springer International Publishing

Erscheinungsdatum: 2016

Einband: Taschenbuch

Zustand: Neu

Über diesen Titel

Inhaltsangabe:

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.