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History The program was developed by Igor V. Tetko with participation of Vasily V. Kovalishyn (Cascade
Correlation algorithm) during 1993-1999. It was changed to support Java interface (developed with Dmitry Filipov) in 1999.
Software
The interface for the browser is done using Java, calculations are done using C++ program. Hardware
The calculations are done on several UNIX servers.
Selected References
Tetko, I. V.; Livingstone, D. J.; Luik, A. I. Neural network studies. 1. Comparison of overfitting and overtraining, J. Chem. Inf. Comput. Sci., 1995, 35, 826-833.
=> The principal article describing neural network implementation.
Tetko, I. V.; Villa, A. E.P. An Enhancement of Generalization Ability in Cascade Correlation Algorithm by Avoidance of Overfitting/Overtraining Problem. Neural Processing Letters1997, 6, 43-50.
=> The same ideas applied to Cascade Correlation networks.
Tetko, I. V.; Villa, A. E.; Aksenova, T. I.; Zielinski, W. L.; Brower, J.; Collantes, E. R.; Welsh, W. J. Application of a pruning algorithm to optimize artificial neural networks for pharmaceutical fingerprinting, J. Chem. Inf. Comput. Sci., 1998, 38, 660-8, download article.
=> The principal article describing neural network implementation.
Kovalishyn, V. V.; Tetko, I. V.; Luik, A. I.; Kholodovych, V. V.; Villa, A. E. P.; Livingstone, D. J. Neural network studies. 3. Variable selection in the cascade-correlation learning architecture, J. Chem. Inf. Comput. Sci., 1998, 38, 651-659, download article.
Tetko, I. V.; Villa, A. E.; Livingstone, D. J. Neural network studies. 2. Variable selection, J. Chem. Inf. Comput. Sci., 1996, 36, 794-803, download article.
=>Two articles about pruning algorithms.
Tetko, I. V.; Villa, A. E. P. Efficient partition of learning data sets for neural network training, Neural Networks, 1997, 10, 1361-1374, download article.
Tetko, I. V.; Villa, A. E. P. An efficient partition of training data set improves speed and accuracy of cascade-correlation algorithm, Neural Processing Letters, 1997, 6, 51-59.
=> Two articles about selection of data for learning/validation sets in neural network application to large data sets.
Livingstone, D. J.; Manallack, D. T.; Tetko, I. V. Data modelling with neural networks: advantages and limitations, J. Comput. Aided Mol. Des., 1997, 11, 135-42.
=> Review article of several approaches in neural network studies.
Tetko, I. V.; Luik, A. I.; Poda, G. I. Applications of neural networks in structure-activity relationships of a small number of molecules, J. Med. Chem., 1993, 36, 811-4, download article.
=>The first "neural network" article of the authors.
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