Statistical modelling in GLIM
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Bayesian Learning Techniques: Application to Neural Networks with Constraints on Weight Space
WIRN VIETRI 2002 Proceedings of the 13th Italian Workshop on Neural Nets-Revised Papers
A novel neural network-based survival analysis model
Neural Networks - 2003 Special issue: Advances in neural networks research IJCNN'03
Computational Statistics & Data Analysis
Artificial neural network for the joint modelling of discrete cause-specific hazards
Artificial Intelligence in Medicine
Analysis of survival data having time-dependent covariates
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Proceedings of the 2009 conference on Computational Intelligence and Bioengineering: Essays in Memory of Antonina Starita
Partial logistic artificial neural networks (PLANN) for flexible modeling of censored survival data
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Patient stratification with competing risks by multivariate fisher distance
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
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Flexible parametric techniques for regression analysis, such as those based on feed forward artificial neural networks (FFANNs), can be useful for the statistical analysis of censored time data. These techniques are of particular interest for the study of the outcome dependence from several variables measured on a continuous scale, since they allow for the detection of complex non-linear and non-additive effects. Few efforts have been made until now to account for censored times in FFANNs. In the attempt to fill this gap, specific error functions and data representation will be introduced for multilayer perceptron and radial basis function extensions of generalized linear models for survival data.