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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Book ChapterDOI
A Cascade Network Algorithm Employing Progressive RPROP
TL;DR: Casper, like Cascor, is a constructive learning algorithm which builds cascade networks, but instead of using weight freezing and a correlation measure to install new neurons, Casper uses a variation of RPROP to train the whole network.
Journal ArticleDOI
Learning flexible sensori-motor mappings in a complex network
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Journal ArticleDOI
Fast initialization for cascade-correlation learning
TL;DR: Empirical simulations show that the new method can significantly speed-up the cascade-correlation learning compared to the case where the candidate training is used, and the overall performance remained similar or was even better than with the candidateTraining.
Journal ArticleDOI
Sequential learning in neural networks: A review and a discussion of pseudorehearsal based methods
TL;DR: This review explores the topic of sequential learning, where information to be learned and retained arrives in separate episodes over time, in the context of artificial neural networks, and examines the pseudorehearsal mechanism, which is an effective solution to the catastrophic forgetting problem in back propagation type networks.
Dissertation
Rapid learning in robotics
TL;DR: A new learning algorithm, the Parameterized Self-Organizing Maps, is derived from a model of neural self-organization that has a number of benefits that make it particularly suited for applications in the field of robotics.
References
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Book ChapterDOI
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Parallel Distributed Processing: Explorations in the Microstructure of Cognition: Foundations
Book
Learning internal representations by error propagation
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Increased Rates of Convergence Through Learning Rate Adaptation
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