An evolutionary approach to training relaxation labeling processes
Pattern Recognition Letters
Autoassociative learning in relaxation labeling networks
Pattern Recognition Letters
Unsupervised Texture Segmentation in a Deterministic Annealing Framework
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Vector Quantization Using the WTA-Rule with Activity Equalization
Neural Processing Letters
Learning Compatibility Coefficients for Relaxation Labeling Processes
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Competitive-Layer Model for Feature Binding and Sensory Segmentation
Neural Computation
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
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We present a combination of unsupervised and supervised learning to generate a compatibilityin teraction for feature binding and labeling problems. We focus on the unsupervised data driven generation of prototypic basis interactions by means of clustering of proximityv ectors, which are computed from pairs of data in the training set. Subsequentlya supervised method recentlyin troduced in [9] is used to determine coefficients to form a linear combination of the basis functions, which then serves as interaction. As special labeling dynamic we use the competitive layer model, a recurrent neural network with linear threshold neurons, and show an application to cell segmentation.