Determining the optimal size of a neural network is complicated. Neural networks, with many free parameters, can be used to solve very complex problems. However, these neural networks are susceptible to overfitting. BCAP (Brantley-Clark Artificial Neural Network Pruning Technique) addresses overfitting by combining duplicate neurons in a neural network hidden layer, thereby forcing the network to learn more distinct features. We compare hidden units using the cosine similarity, and combine those that are similar with each other within a threshold ϵ. By doing so the co-adaption of the neurons in the network is reduced because hidden units that are highly correlated (i.e. similar) are combined. In this paper we show evidence that BCAP is successful in reducing network size while maintaining accuracy, or improving accuracy of neural networks during and after training.
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|Commitee:||Chen, Jian, Clark, Greg|
|School:||University of Maryland, Baltimore County|
|School Location:||United States -- Maryland|
|Source:||MAI 55/05M(E), Masters Abstracts International|
|Subjects:||Artificial intelligence, Computer science|
|Keywords:||Deep learning, Neural network optimization, Neural networks, Pruning neural networks|
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