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http://dx.doi.org/10.5391/JKIIS.2005.15.1.087

Family of Cascade-correlation Learning Algorithm  

Choi Myeong-Bok (국립 원주대학 행정전산과ㆍ여성교양과)
Lee Sang-Un (국립 원주대학 행정전산과ㆍ여성교양과)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.15, no.1, 2005 , pp. 87-91 More about this Journal
Abstract
The cascade-correlation (CC) learning algorithm of Fahlman and Lebiere is one of the most influential constructive algorithm in a neural network. Cascading the hidden neurons results in a network that can represent very strong nonlinearities. Although this power is in principle useful, it can be a disadvantage if such strong nonlinearity is not required to solve the problem. 3 models are presented and compared empirically. All of them are based on valiants of the cascade architecture and output neurons weights training of the CC algorithm. Empirical results indicate the followings： (1) In the pattern classification, the model that train only new hidden neuron to output layer connection weights shows the best predictive ability; (2) In the function approximation, the model that removed input-output connection and used sigmoid-linear activation function is better predictability than CasCor algorithm.
Keywords
Cascade-correlation learning algorithm; Constructive algorithm; Nonlinearity; Activation Function; Predictability;
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