Model selection in neural networks
Neural Networks
Statistics and data mining techniques for lifetime value modeling
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Artificial Neural Networks: Theory and Applications
Artificial Neural Networks: Theory and Applications
Second-Order Methods for Neural Networks
Second-Order Methods for Neural Networks
A Neural Network Model for Prognostic Prediction
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
A comparison of some error estimates for neural network models
Neural Computation
IEEE Transactions on Neural Networks
Reduced Support Vector Machines: A Statistical Theory
IEEE Transactions on Neural Networks
Comparing Support Vector Machines and Feedforward Neural Networks With Similar Hidden-Layer Weights
IEEE Transactions on Neural Networks
Recursive Support Vector Machines for Dimensionality Reduction
IEEE Transactions on Neural Networks
Cross-validation, bootstrap, and support vector machines
Advances in Artificial Neural Systems
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Cox's proportional hazards model has been widely used for the analysis of treatment and prognostic effects with censored survival data. In this paper, we propose a neural network model based on bootstrapping to estimate the survival function and predict the short-term survival at any time during the course of the disease. The bootstrapping for the neural network is introduced when selecting the optimum number of hidden units and testing the goodness-of-fit. The proposed methods are illustrated using data from a long-term study of patients with primary biliary cirrhosis (PBC).