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.
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TextoSeries Lecture Notes in Computer Science, 7005Editor: Berlin, Heidelberg : Springer Berlin Heidelberg, 2011Descripción: XIII, 333 p. online resourceTipo de contenido: text Tipo de medio: computer Tipo de portador: online resourceISBN: 9783642244711Trabajos contenidos: SpringerLink (Online service)Tema(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)Formatos físicos adicionales: Sin títuloClasificación CDD: 006.4 Clasificación LoC:Q337.5TK7882.P3Recursos en línea: de clik aquí para ver el libro electrónico Springer eBooksResumen: 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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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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