Open AccessProceedings Article
The Cascade-Correlation Learning Architecture
Scott E. Fahlman,Christian Lebiere +1 more
- Vol. 2, pp 524-532
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TLDR
The Cascade-Correlation architecture has several advantages over existing algorithms: it learns very quickly, the network determines its own size and topology, it retains the structures it has built even if the training set changes, and it requires no back-propagation of error signals through the connections of the network.Abstract:
Cascade-Correlation is a new architecture and supervised learning algorithm for artificial neural networks. Instead of just adjusting the weights in a network of fixed topology. Cascade-Correlation begins with a minimal network, then automatically trains and adds new hidden units one by one, creating a multi-layer structure. Once a new hidden unit has been added to the network, its input-side weights are frozen. This unit then becomes a permanent feature-detector in the network, available for producing outputs or for creating other, more complex feature detectors. The Cascade-Correlation architecture has several advantages over existing algorithms: it learns very quickly, the network determines its own size and topology, it retains the structures it has built even if the training set changes, and it requires no back-propagation of error signals through the connections of the network.read more
Citations
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Water demand forecasting: review of soft computing methods
TL;DR: It is found that soft computing methods are mainly used for short-term demand forecasting, and it seems soft computing has a lot more to contribute to water demand forecasting.
Book ChapterDOI
Predicting the Mackey-Glass Timeseries With Cascade-Correlation Learning
TL;DR: In this paper, a cascade-correlation learning algorithm has been used to predict realvalued timeseries, and the results of learning to predict the Mackey-glass chaotic timeseries using Cascade-Correlation are compared with other neural net learning algorithms as well as standard techniques.
Journal ArticleDOI
Exploring constructive cascade networks
N.K. Treadgold,T.D. Gedeon +1 more
TL;DR: The acasper algorithm, which incorporates the insights obtained from the empirical studies, was shown to have good generalization and network construction properties and was compared to the cascade correlation algorithm on the Proben 1 and additional regression data sets.
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Connectionist theory refinement: genetically searching the space of network topologies
David W. Opitz,Jude W. Shavlik +1 more
TL;DR: The Regent algorithm as mentioned in this paper uses domain-specific knowledge to help create an initial population of knowledge-based neural networks and genetic operators of crossover and mutation to continually search for better network topologies.
Learning to pronounce written words : a study in inductive language learning
TL;DR: Compression by compression, 23, 144 levels of, 10, 12, 16, 46, 47 activation, 29 propagation, 29 alphabet orthographic, 179 phonemic, 179 alphabetic writing system, 2 analogical modelling.
References
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Parallel Distributed Processing: Explorations in the Microstructure of Cognition: Foundations
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Learning internal representations by error propagation
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Increased Rates of Convergence Through Learning Rate Adaptation
TL;DR: A study of Steepest Descent and an analysis of why it can be slow to converge and four heuristics for achieving faster rates of convergence are proposed.