Improving the discriminatory capabilities of a neural classifier by using a biased-wavelet layer.
In the context of wavelet neural networks (WNN's), two modifications to the basic training algorithms are proposed, namely the introduction of a bias component in the wavelets and the adoption of a weight decay policy. A problem of ECG segment classification is used for illustration purposes. Results suggest that bias improves the discriminatory capabilities of the WNN, which is also compared favourably to a conventional perceptron classifier. The use of weight decay during training, followed by pruning, resulted in a more parsimonious network, which also turned out to be a more conservative classifier. The knowledge embedded in the wavelet layer is interpreted with basis on the concept of super-wavelets.
Galvão
R K
RK
CTA-ITA-Div. Engenharia Eletrônica, São José dos Campos, SP, Brazil.
Yoneyama
T
T
D000465:Algorithms; D015982:Bias; D004192:Discrimination, Psychological; D004562:Electrocardiography; D006801:Humans; D016571:Neural Networks, Computer; D012815:Signal Processing, Computer-Assisted