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.

Format:

Book

Contributor:

Robles-Kelly, Antonio A., editor.

Loog, Marco, editor.

Biggio, Battista, editor.

Escolano, Francisco, editor.

Wilson, Richard (Editor), editor.

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

LNCS sublibrary. Image processing, computer vision, pattern recognition, and graphics ; SL 6, 10029.

Image Processing, Computer Vision, Pattern Recognition, and Graphics ; 10029

Language:

English

Subjects (All):

Artificial intelligence.

Pattern perception.

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.

Local Subjects:

Artificial Intelligence.

Pattern Recognition.

Information Systems Applications (incl. Internet).

Database Management.

Algorithm Analysis and Problem Complexity.

Data Mining and Knowledge Discovery.

Physical Description:

1 online resource (XIII, 588 pages) : 167 illustrations.

Edition:

First edition 2016.

Contained In:

Springer eBooks

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2016.

System Details:

text file PDF

Summary:

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. .

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. .

Other Format:

Printed edition:

ISBN:

978-3-319-49055-7

9783319490557

Access Restriction:

Restricted for use by site license.