A massively parallel architecture for a self-organizing neural pattern recognition machine
Computer Vision, Graphics, and Image Processing
Instance-Based Learning Algorithms
Machine Learning
Learning in the presence of concept drift and hidden contexts
Machine Learning
The impact of changing populations on classifier performance
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Self-Organizing neural networks: recent advances and applications
Self-Organizing neural networks: recent advances and applications
Self-Organizing Maps
Incremental Learning from Noisy Data
Machine Learning
HDGSOM: A Modified Growing Self-Organizing Map for High Dimensional Data Clustering
HIS '04 Proceedings of the Fourth International Conference on Hybrid Intelligent Systems
Learning drifting concepts: Example selection vs. example weighting
Intelligent Data Analysis
Flexible architecture of self organizing maps for changing environments
CIARP'05 Proceedings of the 10th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis and Applications
Robust growing hierarchical self organizing map
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Dynamic self-organizing maps with controlled growth for knowledge discovery
IEEE Transactions on Neural Networks
The growing hierarchical self-organizing map: exploratory analysis of high-dimensional data
IEEE Transactions on Neural Networks
Fusion of self organizing maps
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Pattern Recognition Letters
Robustness analysis of the neural gas learning algorithm
CIARP'06 Proceedings of the 11th Iberoamerican conference on Progress in Pattern Recognition, Image Analysis and Applications
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In this paper we extend the hierarchical self-organizing maps model (HSOM) to address the problem of learning topological drift under non-stationary and noisy environments. The new model, called robust and flexible hierarchical self-organizing maps (RoFlex-HSOM), combines the capabilities of robustness against noise and the flexibility to adapt to the changing environment. The RoFlex-HSOM model consists of a hierarchical tree structure of growing self-organizing maps (SOMs) that adapts its architecture based on the data. The model preserves the topology mapping from the high-dimensional time-dependent input space onto a neuron position in a low-dimensional hierarchical output space grid. Furthermore, the RoFlex-HSOM algorithm has the plasticity to track and adapt to the topological drift, it gradually forgets (but no catastrophically) previous learned patterns and it is resistant to the presence of noise. We empirically show the capabilities of our model with experimental results using synthetic sequential data sets and the ''El Nino'' real world data.