A comparison among output codification schemes

Proceedings of the International Joint Conference on Neural Networks

Volumn 1, Issue , 2001, Pages 61-64

  Hernandez Espinosa C ^a   Fernandez Redondo M ^a  

^a UNIVERSITAT JAUME I   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

CODES (SYMBOLS); CONVERGENCE OF NUMERICAL METHODS; DATABASE SYSTEMS; FEEDFORWARD NEURAL NETWORKS; PROBABILITY;

CODIFICATION SCHEME; MULTILAYER FEEDFORWARD NETWORK; NOISY CODIFICATION;

MULTILAYER NEURAL NETWORKS;

EID: 0034856401     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (7)

1. Improving generalization through multiple redundant output units
2. Training neural networks with additive noise in the desired signal
3. Target adaptation to improve the performance of least-squares classifiers
4. Coding the outputs of a feedforward neural net
5. Solving XOR with a single layered perceptron by supervised self-organization of multiple output labels per class
6. Supervised networks that self-organize class outputs
7. Supervised self-coding in multilayered feedforward networks