Cascade-correlation learning

Touretzky, Ed., Morgan Kaufmann, San Mateo, California, 1990 pp. 524-532. The Cascade-Correlation Learning Architecture. 

Fahlman, S. Lebiere, C. (1990). The cascade-correlation learning architecture. Adv Neural Inf Process Syst 2,524-32. 

Kovalishyn, V.V., Tetko, I.V., Luik, A.I., Kholodovych, V.V., Villa, A.E.P. and Livingstone, D.J. (1998). Neural Network Studies. 3. Variable Selection in the Cascade-Correlation Learning Architecture. J.Chem.Inf.Comput.Sci,38, 651-659. 

Livingstone,. Chem. Inf. Comput. Sci., 38, 651 (1998). Neural Network Studies. 3. Variable Selection in the Cascade-Correlation Learning Architecture. 

Kovalishyn et al. used the cascade correlation neural net to select variables in QSAR smdies (131). Their results suggest that these pmning methods can be successfully used to optimize the set of variables for the cascade-correlation learning algorithm neural networks. The use of variables selected by the elaborated methods provides an improvement of nemal network prediction ability compared to that calculated using the unpruned sets of variables. 

Beale, R. and Jackson T. (1990) Neural Computing - An Introduction, Taylor Francis, London, 256 pp. Fahlman, S.E. and Lebiere, C. (1990) The cascade correlation learning architecture. In Advances in Neural Information Processing Systems, Vol. 2 (ed D.S. Touretzky), Morgan Kauffman Publishers Inc, San Francisco, pp. 524—532. 

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