Daniel C. St. Clair
ABSTRACT
No abstract available.
Index Terms
An evaluation of learning performance in backpropagation neural networks and decision-tree classifier systems
Recommendations
Evaluation of Parameter Settings for Training Neural Networks Using Backpropagation Algorithms: A Study With Clinical Datasets
Artificial neural networks (ANN) are widely used for classification, and the training algorithm commonly used is the backpropagation (BP) algorithm. The major bottleneck faced in the backpropagation neural network training is in fixing the appropriate ...
Neural network learning without backpropagation
The method introduced in this paper allows for training arbitrarily connected neural networks, therefore, more powerful neural network architectures with connections across layers can be efficiently trained. The proposed method also simplifies neural ...
Performance evaluation of artificial neural networks for spatial data analysis
the artificial neural network training algorithm is implemented in MATLAB language. This implementation is focused on the network parameters in order to get the optimal architecture of the network that means (the optimal neural network is the network ...
Comments