Abstract
A neural network architecture is introduced which implements a supervised clustering algorithm for the classification of feature vectors. The network is selforganising, and is able to adapt to the shape of the underlying pattern distribution as well as detect novel input vectors during training. It is also capable of determining the relative importance of the feature components for classification. The architecture is a hybrid of supervised and unsupervised networks, and combines the strengths of three wellknown architectures: learning vector quantisation, backpro-pagation and adaptive resonance theory. Network performance is compared to that of learning vector quantisation, back-propagation and cascade-correlation. It is found that performance is generally as good as or better than the performance of these other architectures, while training time is considerably shorter. However, the main advantage of the hybrid architecture is its ability to gain insight into the feature pattern space.
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Abbreviations
- O j :
-
The output value of thejth unit
- I i :
-
Theith component of the input pattern
- W ij :
-
The weight of the cluster connection between theith input and thejth unit
- B ij :
-
The weight of the shape connection between theith input and thejth unit
- N :
-
The dimension of the input patterns
- v j :
-
The vigilance parameter of thejth unit
- v init :
-
The initial vigilance parameter value
- v rate :
-
The change in the vigilance parameter value
- X i :
-
Theith direction in anN-dimensional coordinate system
- T k :
-
The classification tag of thekth unit
- C :
-
The classification tag of the current input vector
- α(p) :
-
The learning rate at thepth epoch for the cluster weights
- p :
-
The current epoch
- P :
-
The total number of epochs
- E k :
-
The error associated with thekth unit
- β :
-
The constant learning rate for the shape weights
- a j :
-
The age in epochs of thejth unit
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Augusteijn, M.F., Steck, U.J. Supervised adaptive clustering: A hybrid neural network clustering algorithm. Neural Comput & Applic 7, 78–89 (1998). https://doi.org/10.1007/BF01413712
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DOI: https://doi.org/10.1007/BF01413712