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Provides information on a study that presented a semi-nonparametric approach to risk-neutral density extraction from option prices which is based on an extension of the concept of mixture density networks. Option pricing models; Estimation and evaluation of models; Comparison of risk-neutral densities.

Presents a study on recurrent neural networks. Trading of options; Predictive models; Elman recurrent neural network; Discussion on data, volatility measures and trading strategy.

Opinion. Presents a commentary on the article by Valeriy Nenov and Michael Dyer, published in the March 1994 issue of `Connection Science,' which discussed DETE, a model of language learning in neural networks. Strengths of the work by Nenov and Dyer; Concerns on the difference between Katamic memory and classical neural networks.

Purpose: To compare the performance of neural networks with that of linear regression to predict the postoperative effective lens position (ELP) from preoperative biometry measurements.Setting: Departments of Ophthalmology, Medical Cybernetics and Artificial Intelligence, and Medical Physics, Medical University of Vienna, Vienna, Austria.Methods: The neural-network-type multilayer perceptron (MLP) and a linear regression technique were used to predict ELP. Suitable MLP models and variable input combinations were selected by extended-feature subset selection. Apart from the usual preoperative biometric variables, anterior chamber depth and lens thickness were measured with partial coherence interferometry and white-to-white measurements were used as input variables.Results: Prediction of ELP could be improved from a correlation coefficient (Pearson) of 0.54 for linear regression to a coefficient of 0.68 for the MLP; however, this difference was not statistically significant.Conclusion: The prediction of postoperative ACD with the MLP was not significantly better than the prediction using linear regression. [Copyright &y& Elsevier]