Unsupervised Optimal Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Self-Organizing Maps
How to make large self-organizing maps for nonvectorial data
Neural Networks - New developments in self-organizing maps
Generalized relevance learning vector quantization
Neural Networks - New developments in self-organizing maps
A unified framework for model-based clustering
The Journal of Machine Learning Research
Principle of Learning Metrics for Exploratory Data Analysis
Journal of VLSI Signal Processing Systems
Supervised Neural Gas with General Similarity Measure
Neural Processing Letters
Improved learning of Riemannian metrics for exploratory analysis
Neural Networks - 2004 Special issue: New developments in self-organizing systems
Self-organizing maps and clustering methods for matrix data
Neural Networks - 2004 Special issue: New developments in self-organizing systems
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
Fuzzy classification by fuzzy labeled neural gas
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
Self-organizing maps, vector quantization, and mixture modeling
IEEE Transactions on Neural Networks
KI '07 Proceedings of the 30th annual German conference on Advances in Artificial Intelligence
Patch clustering for massive data sets
Neurocomputing
Neural gas clustering for dissimilarity data with continuous prototypes
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
IDA'07 Proceedings of the 7th international conference on Intelligent data analysis
Topographic mapping of large dissimilarity data sets
Neural Computation
Detection of locally relevant variables using SOM-NG algorithm
Engineering Applications of Artificial Intelligence
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Recently, two extensions of neural gas have been proposed: a fast batch version of neural gas for data given in advance, and extensions of neural gas to learn a (possibly fuzzy) supervised classification. Here we propose a batch version for supervised neural gas training which allows to efficiently learn a prototype-based classification, provided training data are given beforehand. The method relies on a simpler cost function than online supervised neural gas and leads to simpler update formulas. We prove convergence of the algorithm in a general framework, which also incorporates supervised k-means and supervised batch-SOM, and which opens the way towards metric adaptation as well as application to proximity data not embedded in a real-vector space.