Advances in Self-Organizing Maps and Learning Vector Quantization

Chapter 1 How Many Dissimilarity/Kernel Self Organizing Map Variants Do We Need?

Chapter 2 Dynamic Formation of Self-Organizing Maps

Chapter 3 MS-SOM: Magnitude Sensitive Self-Organizing Maps

Chapter 4 Bagged Kernel SOM

Chapter 5 Probability Ridges and Distortion Flows: Visualizing Multivariate Time Series Using a Variational Bayesian Manifold Learning Method

Chapter 6 Short Review of Dimensionality Reduction Methods Based on Stochastic Neighbour Embedding

Chapter 7 Attention Based Classification Learning in GLVQ and Asymmetric Misclassification Assessment

Chapter 8 Visualization and Classification of DNA Sequences Using Pareto Learning Self Organizing Maps Based on Frequency and Correlation Coefficient

Chapter 9 Probabilistic Prototype Classification Using t-norms

Chapter 10 Rejection Strategies for Learning Vector Quantization – A Comparison of Probabilistic and Deterministic Approaches

Chapter 11 Prototype-Based Classifiers and Their Application in the Life Sciences

Chapter 12 Generative versus Discriminative Prototype Based Classification

Chapter 13 Some Room for GLVQ: Semantic Labeling of Occupancy Grid Maps

Chapter 14 Anomaly Detection Based on Confidence Intervals Using SOM with an Application to Health Monitoring

Chapter 15 RFSOM – Extending Self-Organizing Feature Maps with Adaptive Metrics to Combine Spatial and Textural Features for Body Pose Estimation

Chapter 16 Beyond Standard Metrics – On the Selection and Combination of Distance Metrics for an Improved Classification of Hyperspectral Data

Chapter 17 The Sky Is Not the Limit

Chapter 18 Development of Target Reaching Gesture Map in the Cortex and Its Relation to the Motor Map: A Simulation Study

Chapter 19 A Concurrent SOM-Based Chan-Vese Model for Image Segmentation

Chapter 20 Five-Dimensional Sentiment Analysis of Corpora, Documents and Words

Chapter 21 SOMbrero: An R Package for Numeric and Non-numeric Self-Organizing Maps

Chapter 22 K-Nearest Neighbor Nonnegative Matrix Factorization for Learning a Mixture of Local SOM Models

Chapter 23 Comparison of Spectrum Cluster Analysis with PCA and Spherical SOM and Related Issues Not Amenable to PCA

Chapter 24 Exploiting the Structures of the U-Matrix

Chapter 25 Partial Mutual Information for Classification of Gene Expression Data by Learning Vector Quantization

Chapter 26 Composition of Learning Patterns Using Spherical Self-Organizing Maps in Image Analysis with Subspace Classifier

Chapter 27 Self-Organizing Map for the Prize-Collecting Traveling Salesman Problem

Chapter 28 A Survey of SOM-Based Active Contour Models for Image Segmentation

Chapter 29 Advances in Self-Organizing Maps and Learning Vector Quantization