Abstract
A feedforward neural network architecture aimed at survival probability estimation is presented which generalizes the standard, usually linear, models described in literature. The network builds an approximation to the survival probability of a system at a given time, conditional on the system features. The resulting model is described in a hierarchical Bayesian framework. Experiments with synthetic and real world data compare the performance of this model with the commonly used standard ones.
Original language | English |
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Pages (from-to) | 855-864 |
Number of pages | 10 |
Journal | Neural Networks |
Volume | 16 |
Issue number | 5-6 |
DOIs | |
Publication status | Published - Jun 2003 |
Keywords
- Bayesian learning
- Conditioning probability estimation
- MCMC methods
- Neural networks
- Survival analysis
ASJC Scopus subject areas
- Artificial Intelligence
- Neuroscience(all)