Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Measures for the organization of self-organizing maps
Self-Organizing neural networks
Self-Organizing Maps
Comparing Self-Organizing Maps
ICANN 96 Proceedings of the 1996 International Conference on Artificial Neural Networks
Neural Computation
Application of Topology Preserving Ensembles for Sensory Assessment in the Food Industry
IDEAL '08 Proceedings of the 9th International Conference on Intelligent Data Engineering and Automated Learning
Fusion of self organizing maps
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Boosting unsupervised competitive learning ensembles
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Quality of adaptation of fusion ViSOM
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
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Topology preserving mappings are great tools for data visualization and inspection in large datasets. This research presents a study of the combination of different ensemble training techniques with a novel summarization algorithm for ensembles of topology preserving models. The aim of these techniques is the increase of the truthfulness of the visualization of the dataset obtained by this kind of algorithms and, as an extension, the stability conditions of the former. A study and comparison of the performance of some novel and classical ensemble techniques, using well-known datasets from the UCI repository (Iris and Wine), are presented in this paper to test their suitability, in the fields of data visualization and topology preservation when combined with one of the most widespread of that kind of models such as the Self-Organizing Map.