A DIFFERENTIAL GEOMETRY-BASED NEURODYNAMICAL CLASSIFIER UDC531/534:611.69:514.76(045)=111
Tijana T. Ivancevic, Murk J. Bottema, Lakhmi C. Jain · 2007
A new model for a neurodynamical classifier is proposed. The classifier is viewed as a generalized bi-directional associative memory (GBAM) (11) and is described in the language of differential geometry (12-14). GBAM is a tensor-field system resembling a two-phase biological neural oscillator in which an excitatory neural field excites an inhibitory neural field, which reciprocally inhibits the excitatory one. GBAM equations have been directly implemented in the computer algebra system 'Mathematica' and tested on two different sets of data related to detection of breast cancer.• The GBAM classifier outperformed other neural classifiers.