Abstract
Recently there has been a growing interest in the application of neural networks in the field of QSAR. It was demonstrated that this method is often superior to the traditional approaches.1 Other studies have shown that prediction ability of such methods can be substantially improved if the number of input variables for neural networks is optimized.2,3
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
D. Maddalena, Applications of artificial neural networks to quantitative structure-activity relationships, Exp. Opin. Thee. Patents. 6: 239–251 (1996).
I.V. Tetko, A.E.P. Villa, and D.J. Livingstone, Neural network studies. 2. Variable selection, J. Chem. Inf. Comput. Sci. 36: 794–803 (1996).
D.J. Maddalena and S.A.R. Johnston, Prediction of receptor properties and binding affinity of ligands to benzodiazepines/gabaa receptors using artificial neural networks, J. Med. Chem. 38: 715–724 (1995).
S. Demon, E. Bienenstock, and R. Dourstat, Neural networks and the bias/variance dilemma, Neural Computation 4: 1–58 (1992).
I.V. Tetko, D.T. Livingstone, and A.I. Luik, Neural network studies. I. Comparison of overfitting and overtraining, J. Chem. Inf. Comput. Sri. 35: 826–833 (1995).
T. Ash and G. Cottrell, Topology-modifying neural network algorithms, in: The Handbook of Brain Theory and Neural Networks, M.A. Arbib, ed., MIT, Cambridge, (1995).
S.E. Fahlman and C. Lebiere, The cascade-correlation learning architecture, in: NIPS°2, D.S. Touretzky, cd., Morgan-Kaufmann, San Mateo (1990).
V.V. Kovalishyn, LV. Tetko, A.I. Luik, V.V. Kholodovych, A.E.P. Villa, and D.J. Livingstone, Neural network studies. 3. Variable selection in the cascade-correlation learning architecture, J. Chem. Inf. Comput. Sci. 38: 651–659 (1998).
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2000 Springer Science+Business Media New York
About this chapter
Cite this chapter
Tetko, I.V., Kovalishyn, V.V., Luik, A.I., Kasheva, T.N., Villa, A.E.P., Livingstone, D.J. (2000). Variable Selection in the Cascade-Correlation Learning Architecture. In: Gundertofte, K., Jørgensen, F.S. (eds) Molecular Modeling and Prediction of Bioactivity. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-4141-7_124
Download citation
DOI: https://doi.org/10.1007/978-1-4615-4141-7_124
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4613-6857-1
Online ISBN: 978-1-4615-4141-7
eBook Packages: Springer Book Archive