Structural, Syntactic, and Statistical Pattern Recognition Joint IAPR International Workshop, SSPR&SPR 2012, Hiroshima, Japan, November 7-9, 2012. Proceedings /

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Gimel’farb, Georgy (Επιμελητής έκδοσης), Hancock, Edwin (Επιμελητής έκδοσης), Imiya, Atsushi (Επιμελητής έκδοσης), Kuijper, Arjan (Επιμελητής έκδοσης), Kudo, Mineichi (Επιμελητής έκδοσης), Omachi, Shinichiro (Επιμελητής έκδοσης), Windeatt, Terry (Επιμελητής έκδοσης), Yamada, Keiji (Επιμελητής έκδοσης)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2012.
• Σειρά: Lecture Notes in Computer Science, 7626
• Θέματα: 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 Discovery.
• Φυσική περιγραφή: XVIII, 755 p. 236 illus. online resource.
• ISBN: 9783642341663
• ISSN: 0302-9743 ;

This volume constitutes the refereed proceedings of the Joint IAPR International Workshops on Structural and Syntactic Pattern Recognition (SSPR 2012) and Statistical Techniques in Pattern Recognition (SPR 2012), held in Hiroshima, Japan, in November 2012 as a satellite event of the 21st International Conference on Pattern Recognition, ICPR 2012. The 80 revised full papers presented together with 1 invited paper and the Pierre Devijver award lecture were carefully reviewed and selected from more than 120 initial submissions. The papers are organized in topical sections on structural, syntactical, and statistical pattern recognition, graph and tree methods, randomized methods and image analysis, kernel methods in structural and syntactical pattern recognition, applications of structural and syntactical pattern recognition, clustering, learning, kernel methods in statistical pattern recognition, kernel methods in statistical pattern recognition, as well as applications of structural, syntactical, and statistical methods.