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Record Nr. |
UNINA9910483586303321 |
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Titolo |
Structural, Syntactic, and Statistical Pattern Recognition : Joint IAPR International Workshop, S+SSPR 2016, Mérida, Mexico, November 29 - December 2, 2016, Proceedings / / edited by Antonio Robles-Kelly, Marco Loog, Battista Biggio, Francisco Escolano, Richard Wilson |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016 |
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ISBN |
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Edizione |
[1st ed. 2016.] |
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Descrizione fisica |
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1 online resource (XIII, 588 p. 167 illus.) |
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Collana |
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Image Processing, Computer Vision, Pattern Recognition, and Graphics ; ; 10029 |
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Disciplina |
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Soggetti |
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Artificial intelligence |
Pattern recognition |
Application software |
Database management |
Algorithms |
Data mining |
Artificial Intelligence |
Pattern Recognition |
Information Systems Applications (incl. Internet) |
Database Management |
Algorithm Analysis and Problem Complexity |
Data Mining and Knowledge Discovery |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Nota di contenuto |
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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. . |
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Sommario/riassunto |
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This book constitutes the proceedings of the Joint IAPR International |
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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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