Advances in Self-Organizing Maps and Learning Vector Quantization
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Advances in Self-Organizing Maps and Learning Vector Quantization

Proceedings of the 10th International Workshop, WSOM 2014, Mittweida, Germany, July, 2-4, 2014
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Thomas Villmann
295, Advances in Intelligent Systems and Computing
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How Many Dissimilarity/Kernel Self Organizing Map Variants Do We Need.- Dynamic formation of self-organizing maps.- MS-SOM: Magnitude Sensitive Self-Organizing Maps.- Bagged Kernel SOM.- Probability ridges and distortion flows: Visualizing multivariate time series using a variational Bayesian manifold learning method.- Short review of dimensionality reduction methods based on stochastic neighbour embedding.- Attention based Classification Learning in GLVQ and Asymmetric Classification Error Assessment.-Visualization and Classification of DNA sequences using Pareto learning Self Organizing Maps based on Frequency and Correlation Coefficient.- Probabilistic prototype classification using t-norms.- Rejection Strategies for Learning Vector Quantization - a Comparison of Probabilistic and Deterministic Approaches.- Comparison of spectrum cluster analysis with PCA and spherical SOM and related issues not amenable to PCA.- Exploiting the structures of the U-matrix.- Partial Mutual Information for Classification Analysis of Gene expression Data by Learning Vector Quantization.- Composition of Learning Patterns using Spherical Self-Organizing Maps in Image Analysis with Subspace Classifier.- Self-Organizing Map for the Prize-Collecting Traveling Salesman Problem.- A Survey of SOM-based Active Contour Models for Image Segmentation.- Biologically Plausible SOM Representation of the Orthographic Form of 50,000 French Words.- Prototype-based classifiers and their application in the life sciences.- Generative versus discriminative prototype based classification.- Some room for GLVQ: Semantic Labeling of occupancy grid maps.- Anomaly detection based on confidence intervals using SOM with an application to Health Monitoring.- RFSOM - Extending Self-Organizing feature Maps with adaptive metrics to combine spatial and textural features for body pose estimation.- Beyond Standard Metrics - On the Selection and Combination of Distance Metrics for an Improved.- Classification of Hyperspectral Data.- The Sky Is Not the Limit.- Development of Target Reaching Gesture Map in the Cortex and Its Relation to the Motor Map: A Simulation Study.- A Concurrent SOM-based Chan-Vese Model for Image Segmentation.- Text mining of life-philosophicl insights.- SOMbrero: an R Package for Numeric and Non-numeric Self-Organizing Maps.- K-Nearest Neighbor Nonnegative Matrix Factorization for Learning a Mixture of Local SOM Models.
The book collects the scientific contributions presented at the 10th Workshop on Self-Organizing Maps (WSOM 2014) held at the University of Applied Sciences Mittweida, Mittweida (Germany, Saxony), on July 2-4, 2014. Starting with the first WSOM-workshop 1997 in Helsinki this workshop focuses on newest results in the field of supervised and unsupervised vector quantization like self-organizing maps for data mining and data classification.

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