Advances in Self Organizing Maps and Learning Vector Quantization Proceedings of the 10th International Workshop WSOM 2014 Mittweida Germany July 2 4 2014 1st Edition Thomas Villmann

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 Ba

Short Review of Dimensionality Reduction Methods Based on Stochastic Neighbour Embedding

Attention Based Classification Learning in GLVQ and Asymmetric Misclassification Assessment

Visualization and Classification of DNA Sequences Using Pareto Learning Self Organizing Maps Based o

Probabilistic Prototype Classification Using t-norms

Rejection Strategies for Learning Vector Quantization – A Comparison of Probabilistic and Determin

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 Textur

Beyond Standard Metrics – On the Selection and Combination of Distance Metrics for an Improved Cla

Development of Target Reaching Gesture Map in the Cortex and Its Relation to the Motor Map: A Simula

A Concurrent SOM-Based Chan-Vese Model for Image Segmentation

TheCSOM-CV Model