Topology representing networks
Neural Networks
ACM Computing Surveys (CSUR)
On the Characteristics of Growing Cell Structures (GCS) Neural Network
Neural Processing Letters
A vector space model for automatic indexing
Communications of the ACM
Proceedings of the sixth ACM symposium on Solid modeling and applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Fast and Efficient Projection-Based Approach for Surface Reconstruction
SIBGRAPI '02 Proceedings of the 15th Brazilian Symposium on Computer Graphics and Image Processing
A Self-Organizing Network that Can Follow Non-stationary Distributions
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
A self-organising network that grows when required
Neural Networks - New developments in self-organizing maps
Using Growing Cell Structures for Surface Reconstruction
SMI '03 Proceedings of the Shape Modeling International 2003
Robust growing neural gas algorithm with application in cluster analysis
Neural Networks - 2004 Special issue: New developments in self-organizing systems
On Growing Self - Organizing Neural Networks without Fixed Dimensionality
CIMCA '06 Proceedings of the International Conference on Computational Inteligence for Modelling Control and Automation and International Conference on Intelligent Agents Web Technologies and International Commerce
Journal of Biomedical Informatics - Special section: JAMA commentaries
Engineering Applications of Artificial Intelligence
A self-organizing neural network for detecting novelties
Proceedings of the 2007 ACM symposium on Applied computing
Market segmentation based on hierarchical self-organizing map for markets of multimedia on demand
Expert Systems with Applications: An International Journal
Visual novelty detection with automatic scale selection
Robotics and Autonomous Systems
A Neural Network Based Framework for Directional Primitive Extraction
Neural Processing Letters
Three-dimensional surface reconstruction using meshing growing neural gas (MGNG)
The Visual Computer: International Journal of Computer Graphics
An End-to-End Administrative Document Analysis System
DAS '08 Proceedings of the 2008 The Eighth IAPR International Workshop on Document Analysis Systems
Journal of Signal Processing Systems
Review article: Local adaptive receptive field self-organizing map for image color segmentation
Image and Vision Computing
Surface Reconstruction Method Based on a Growing Self-Organizing Map
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
GNG-SVM framework: classifying large datasets with support vector machines using growing neural gas
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Growing self-reconstruction maps
IEEE Transactions on Neural Networks
Surveillance and human-computer interaction applications of self-growing models
Applied Soft Computing
Functional Segmentation of Renal DCE-MRI Sequences Using Vector Quantization Algorithms
Neural Processing Letters
Learning 2D hand shapes using the topology preservation model GNG
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Neural Computing and Applications - Special Issue on ICONIP2009
IEEE Transactions on Information Technology in Biomedicine
IEEE Transactions on Information Technology in Biomedicine
Constructing maps for mobile robot navigation based on ultrasonic range data
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
The growing hierarchical self-organizing map: exploratory analysis of high-dimensional data
IEEE Transactions on Neural Networks
New adaptive color quantization method based on self-organizing maps
IEEE Transactions on Neural Networks
An Adaptive Learning Approach for 3-D Surface Reconstruction From Point Clouds
IEEE Transactions on Neural Networks
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A number of research studies considering a self-organizing map have been developed since such a map was proposed by Kohonen [1982]. Some of these studies concern SOM-based models that do not use pre-defined structures to produce their mappings. We call these models Self-Organizing Maps with Time-Varying Structure (SOM-TVS). Despite the large number of SOM-TVS models there is not a standard way to describe them. In this article, we propose a framework to describe SOM-TVS models, which we use to describe some of these models and to compare their algorithms, and we present some real-world applications of the models presented.