Advances in Self-Organizing Maps and Learning Vector Quantization

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 gridmaps.- 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

Die vorliegende Arbeit entstand im wesentlichen am Institut für Informatik der Universität Leipzig in der Forschungsgruppe Neuroinformatik, der der Autor von 1991 bis 1994 angehörte. Sie wurde 1996 als Dissertationsschrift angenommen, der Betreuer war Dr. habil. Ralf Der. Neben der Theorie und Anwendung neuronaler Netze arbeitet der Autor auf den Gebieten Genetische Algorithmen, maschinelle Lernverfahren, Optimierung und Statistik.