Read more
This book constitutes the proceedings of the Second International Workshop on Similarity Based Pattern Analysis and Recognition, SIMBAD 2013, which was held in York, UK, in July 2013.The 18 papers presented were carefully reviewed and selected from 33 submissions. They cover a wide range of problems and perspectives, from supervised to unsupervised learning, from generative to discriminative models, from theoretical issues to real-world practical applications, and offer a timely picture of the state of the art in the field.
List of contents
Pattern Learning and Recognition on Statistical Manifolds: An Information-Geometric Review.- Dimension Reduction Methods for Image Pattern Recognition.- Efficient Regression in Metric Spaces via Approximate Lipschitz Extension.- Data Analysis of (Non-)Metric Proximities at Linear Costs.- On the Informativeness of Asymmetric Dissimilarities.- Information-Theoretic Dissimilarities for Graphs.- Information Theoretic Pairwise Clustering.- Correlation Clustering with Stochastic Labellings.- Break and Conquer: Efficient Correlation Clustering for Image Segmentation.- Multi-task Averaging via Task Clustering.- Modeling and Detecting Community Hierarchies.- Graph Characterization Using Gaussian Wave Packet Signature.- Analysis of the Schrödinger Operator in the Context of Graph Characterization.- Attributed Graph Similarity from the Quantum Jensen-Shannon Divergence.- Entropy and Heterogeneity Measures for Directed Graphs.- Fast Learning of Gamma Mixture Models with k-MLE.- Exploiting Geometry in Counting Grids.- On the Dissimilarity Representation and Prototype Selection for Signature-Based Bio-cryptographic Systems.- A Repeated Local Search Algorithm for BiClustering of Gene Expression Data.
Summary
This book constitutes the proceedings of the Second International Workshop on Similarity Based Pattern Analysis and Recognition, SIMBAD 2013, which was held in York, UK, in July 2013.
The 18 papers presented were carefully reviewed and selected from 33 submissions. They cover a wide range of problems and perspectives, from supervised to unsupervised learning, from generative to discriminative models, from theoretical issues to real-world practical applications, and offer a timely picture of the state of the art in the field.
