Here we consider a new approach for weight initialization in cascade-correlation learning. The proposed method is based on the concept of stepwise regression.
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Empirical simulations show that the new method can significantly speed-up the cascade- correlation learning compared to the case where the candidate training is ...
Weight initialization in cascade-correlation learning is considered. Most of the previous studies use the so called candidate training to deal with the ...
Here we consider a new approach for weight initialization in cascade-correlation learning. The proposed method is based on the concept of stepwise regression.
Translated title of the contribution, Fast Initialization for Cascade-Correlation Learning. Original language, English. Pages (from-to), 410-414.
1. Initialize the network. Set up initial CC architecture. 2. Train output layer. The output layer weights are optimized by the genetic al ...
Jan 7, 2016 · To create a new hidden unit, we begin with a candidate unit that receives trainable input connections from all of the network's external inputs and from all ...
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Bibliographic details on Fast initialization for cascade-correlation learning.
These methods speed up the training of ANNs by using decision trees to initialize the weights of ANNs. Show abstract.
Jul 29, 2020 · CasCor starts with a one-layer network, i.e. we will be using a single output neuron and connect that to our input (and bias). To start training ...