Multichannel data aggregation by layers of formal neurons

Authors:
Leon Bobrowski
Affiliations:
Faculty of Computer Science, Białystok Technical University, Poland
Venue:
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
Year:
2006

Citing 3
Cited 0

Design of piecewise linear classifiers from formal neurons by a basis exchange technique

Pattern Recognition
Piecewise-Linear Classifiers, Formal Neurons and Separability of the Leamig Sets

ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume IV-Volume 7472 - Volume 7472
Pattern Classification (2nd Edition)

Pattern Classification (2nd Edition)

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Abstract

Principles of separable aggregation of multichannel (multisource) data sets by parallel layers of formal neurons are considered in the paper. Each data set contains such feature vectors which represent objects assigned to one of a few categories.The term multichannel data sets means that each single object is characterised by data obtained through different information channels and represented by feature vectors in a different feature space. Feature vectors from particular feature spaces are transformed by layers of formal neurons what results in the aggregation of some feature vectors. The postulate of separable aggregation is aimed at the minimization of the number of different feature vectors under the condition of preserving the categories separabilty.