Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Networks with trainable amplitude of activation functions
Neural Networks
Feature Extraction & Image Processing, Second Edition
Feature Extraction & Image Processing, Second Edition
Simple and effective connectionist nonparametric estimation of probability density functions
ANNPR'06 Proceedings of the Second international conference on Artificial Neural Networks in Pattern Recognition
Pattern classification and clustering: A review of partially supervised learning approaches
Pattern Recognition Letters
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A growing interest toward automatic, computer-based tools has been spreading among forensic scientists and anthropologists wishing to extend the armamentarium of traditional statistical analysis and classification techniques. The combination of multiple paradigms is often required in order to fit the difficult, real-world scenarios involved in the area. The paper presents a comparison of combination techniques that exploit neural networks having a probabilistic interpretation within a Bayesian framework, either as models of class-posterior probabilities or as class-conditional density functions. Experiments are reported on a severe sex determination task relying on 1400 scout-view CT-scan images of human crania. It is shown that connectionist probability estimates yield higher accuracies than traditional statistical algorithms. Furthermore, the performance benefits from proper mixtures of neural models, and it turns up affected by the specific combination technique adopted.