Optimal linear combinations of neural networks
Neural Networks
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 02
A new technique for combining multiple classifiers using the dempster-shafer theory of evidence
Journal of Artificial Intelligence Research
Hi-index | 0.00 |
The paper presents methods of classification based on a sequence of feature vectors extracted from signal generated by the object. The feature vectors are assumed to be probabilistic independent. Each feature vector is separately classified by a multilayer perceptron giving a set of local classification decisions. This set of statistical independent decisions is a base for a global classification rule. The rule is derived from statistical decision theory. According to it, an object belongs to a class for which product of corresponding neural network outputs is the largest. The neural outputs are modified in a way to prevent them vanishing to zero. The performance of the proposed rule was tested in an automatic, text independent, speaker identification task. Achieved results are presented.