Similarity-Based Pattern Recognition [electronic resource] :First International Workshop, SIMBAD 2011, Venice, Italy, September 28-30, 2011. Proceedings / edited by Marcello Pelillo, Edwin R. Hancock.
by Pelillo, Marcello [editor.]; Hancock, Edwin R [editor.]; SpringerLink (Online service).
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BookSeries: Lecture Notes in Computer Science: 7005Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2011.Description: XIII, 333 p. online resource.ISBN: 9783642244711.Subject(s): Computer science | Computer software | Database management | Artificial intelligence | Computer vision | Optical pattern recognition | Computer Science | Pattern Recognition | Image Processing and Computer Vision | Artificial Intelligence (incl. Robotics) | Database Management | Algorithm Analysis and Problem Complexity | Information Systems Applications (incl. Internet)DDC classification: 006.4 Online resources: Click here to access online
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Springer eBooksSummary: This book constitutes the proceedings of the First International Workshop on Similarity Based Pattern Recognition, SIMBAD 2011, held in Venice, Italy, in September 2011. The 16 full papers and 7 poster papers presented were carefully reviewed and selected from 35 submissions. The contributions are organized in topical sections on dissimilarity characterization and analysis; generative models of similarity data; graph-based and relational models; clustering and dissimilarity data; applications; spectral methods and embedding.
| Item type | Current location | Call number | Status | Date due | Barcode |
|---|---|---|---|---|---|
| TK7882.P3 (Browse shelf) | Available | ||||
| Long Loan | MAIN LIBRARY | Q337.5 (Browse shelf) | Available |
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This book constitutes the proceedings of the First International Workshop on Similarity Based Pattern Recognition, SIMBAD 2011, held in Venice, Italy, in September 2011. The 16 full papers and 7 poster papers presented were carefully reviewed and selected from 35 submissions. The contributions are organized in topical sections on dissimilarity characterization and analysis; generative models of similarity data; graph-based and relational models; clustering and dissimilarity data; applications; spectral methods and embedding.
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