• Title/Summary/Keyword: (Recurrent) Cascade Correlation Algorithm

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Improvement of Learning Capability with Combination of the Generalized Cascade Correlation and Generalized Recurrent Cascade Correlation Algorithms (일반화된 캐스케이드 코릴레이션 알고리즘과 일반화된 순환 캐스케이드 코릴레이션 알고리즘의 결합을 통한 학습 능력 향상)

  • Lee, Sang-Wha;Song, Hae-Sang
    • The Journal of the Korea Contents Association
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    • v.9 no.2
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    • pp.97-105
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    • 2009
  • This paper presents a combination of the generalized Cascade Correlation and generalized Recurrent Cascade Correlation learning algorithms. The new network will be able to grow with vertical or horizontal direction and with recurrent or without recurrent units for the quick solution of the pattern classification problem. The proposed algorithm was tested learning capability with the sigmoidal activation function and hyperbolic tangent activation function on the contact lens and balance scale standard benchmark problems. And results are compared with those obtained with Cascade Correlation and Recurrent Cascade Correlation algorithms. By the learning the new network was composed with the minimal number of the created hidden units and shows quick learning speed. Consequently it will be able to improve a learning capability.

Generalization of Recurrent Cascade Correlation Algorithm and Morse Signal Experiments using new Activation Functions (순환 케스케이드 코릴레이션 알고리즘의 일반화와 새로운 활성화함수를 사용한 모스 신호 실험)

  • Song Hae-Sang;Lee Sang-Wha
    • Journal of Intelligence and Information Systems
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    • v.10 no.2
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    • pp.53-63
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    • 2004
  • Recurrent-Cascade-Correlation(RCC) is a supervised teaming algorithm that automatically determines the size and topology of the network. RCC adds new hidden neurons one by one and creates a multi-layer structure in which each hidden layer has only one neuron. By second order RCC, new hidden neurons are added to only one hidden layer. These created neurons are not connected to each other. We present a generalization of the RCC Architecture by combining the standard RCC Architecture and the second order RCC Architecture. Whenever a hidden neuron has to be added, the new RCC teaming algorithm automatically determines whether the network topology grows vertically or horizontally. This new algorithm using sigmoid, tanh and new activation functions was tested with the morse-benchmark-problem. Therefore we recognized that the number of hidden neurons was decreased by the experiments of the RCC network generalization which used the activation functions.

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