Cascade correlation

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. 

In many cases it is difficult to determine in advance how many hidden layers and how many HL PEs are required for satisfactory performance. A trial-and-error method to determine this information can be very time-consuming. Cascade correlation networks build HLs one PE at a time, solving a problem incrementally. ... 

C. Cai and P. d. B. Harrington, Anal. Chem., 71, 4134-4141 (1999). Prediction of Substructure and Toxicity of Pesticides with Temperature Constrained-Cascade Correlation Network from Low-Resolution Mass Spectra. 

The cascade correlation architecture was proposed by Fahhnan and Lebiere (1990). The process of network building starts with a one-layer neural network and hidden neurons are added as needed. The network architecture is shown in Fig. 19.27. In each training step, a new hidden neuron is added and its weights are adjusted to maximize the magnitude... 

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. 

Anderson et al used LIBS spectra and three multivariate methods to perform quantitative chemical analysis of rocks. The methods used were PLS, multilayer perceptron artificial neural networks (MLP ANNs) and cascade correlation (CC) ANNs. Precision and accuracy were influenced by the ratio of laser beam diameter (490 pm) to grain size, with coarse-grained rocks often resulting in lower accuracy and precision than analyses of fine-grained rocks and powders. 

Anderson et al applied three multivariate methods [PLS, multilayer perceptron (MLP-ANNs) and cascade correlation (CC-ANNs)] to LIBS data in order to perform quantitative chemical analyses of rocks. 

Good examples of dynamic neural networks are the cascade correlation (Fahhnan and Lebiere, 1990), which extends the MLP to add structure to its network the growing neural gas with utility (GNG-U) (Fritzke, 1997), which uses a SOM-like structure to leam the shape of the data and the plastic self-organizing map (PSOM) (Lang and Warwick, 2002), which is very similar to the SOM except that the neurons are not fixed in a grid and new neurons may be added and removed. The PSOM will be described here. 

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. 

Despite the decision to continue working with cascade correlation ANNs, there was a recognized need to change the way our ANNs were structured in order to address two separate issues one a conunon criticism of automated identification systems in general and the other specific to back-propagation approaches ... 

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