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Analytical feature extraction and spectral summation

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2 Author(s)
T. Windeatt ; Dept. of Electron. Eng., Surrey Univ., Guildford, UK ; R. Tebbs

We propose a formalism for analysing multilayer perceptron (MLP) networks as propagations of binary transitions along excitatory and inhibitory sensitised paths. By characterising a Boolean function as sets of detected transitions, we produce a spectral summation and construct a network from the derived weight constraints. We build hidden node feature detectors by incorporating k-monotonicity checks in the partitioning step of a constructive algorithm. Propagation constraints are also used in an MLP network using gradient descent learning to limit hyperplane movement in weight space. Results for a pattern classification task represented as a binary-to-binary mapping show improved convergence and generalisation performance

Published in:

Pattern Recognition, 1996., Proceedings of the 13th International Conference on  (Volume:4 )

Date of Conference:

25-29 Aug 1996