A Novel Approach to Training Neurons with Dynamic Relational Learning

2019-02-06T03:03:12Z (GMT) by Bernadette M. Garner
Data mining techniques have become extremely important with the proliferation of data. One technique that has attracted much attention is the use of feedforward neural networks. This is because feedforward neural networks are excellent at finding relationships between the input and the output in data sets that are not understood. As a result they are commonly used for function approximation and classification for their ability to generalize. However, the traditional training methods for feedforward neural networks have meant that it is difficult to determine what the network has learnt and can lead to exponential training times if the data can be learnt at all. Long training times are a result of the network being of fixed-size, which can mean the network is either too small to learn the data or too large to learn it well. [...]<br>



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