Competitive learning algorithms for vector quantization
Neural Networks
Representation-burden Conservation Network Applied to Learning VQ (NPL270)
Neural Processing Letters
Self-organizing maps
Improved Representation-burden Conservation Network for LearningNon-stationary VQ
Neural Processing Letters
The handbook of brain theory and neural networks
Codeword distribution for frequency sensitive competitive learning with one-dimensional input data
IEEE Transactions on Neural Networks
Diffusion approximation of frequency sensitive competitive learning
IEEE Transactions on Neural Networks
A Multi-purpose Visual Classification System
Proceedings of the International Conference, 7th Fuzzy Days on Computational Intelligence, Theory and Applications
Visual Checking of Grasping Positions of a Three-Fingered Robot Hand
ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
Data Driven Generation of Interactions for Feature Binding and Relaxation Labeling
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Focus-of-Attention from Local Color Symmetries
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining spatial and colour information for content based image retrieval
Computer Vision and Image Understanding - Special issue on color for image indexing and retrieval
Unsupervised image categorization
Image and Vision Computing
Integrating context-free and context-dependent attentional mechanisms for gestural object reference
ICVS'03 Proceedings of the 3rd international conference on Computer vision systems
Learning compatibility functions for feature binding and perceptual grouping
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Recognition of gestural object reference with auditory feedback
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
| Hi-index | 0.00 |
We propose a new algorithm for vector quantization, the Activity Equalization Vector quantization (AEV). It is based on the winner takes all rule with an additional supervision of the average node activities over a training interval and a subsequent re-positioning of those nodes with low average activities. The re-positioning is aimed to both an exploration of the data space and a better approximation of already discovered data clusters by an equalization of the node activities. We introduce a learning scheme for AEV which requires as previous knowledge about the data only their bounding box. Using an example of Martinetz et al. [1], AEV is compared with the Neural Gas, Frequency Sensitive Competitive Learning (FSCL) and other standard algorithms. It turns out to converge much faster and requires less computational effort.