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Model Selection in Artificial Neural Network

원문정보

초록

영어

Artificial neural network is inspired by the biological neural network. For simplicity, in computer science, it is represented as a set of layers. Many research has been made in evaluating the number of neurons in the hidden layer but still, none was accurate. Several methods are used until now which do not provide the exact formula for calculating the number of the hidden layer as well as the number of neurons in each hidden layer. In this paper model selection approach was presented. Proposed model is based on geographical analysis of decision boundary. Proposed model selection method is useful when we know the distribution of the training data set. To evaluate the performance of the proposed method we compare it to the traditional architecture on IRIS classification problem. According to the experimental result on Iris data proposed method is turned out to be a powerful one.

목차

Abstract
1. Introduction
2. Previous Research
3. Theory : Geographical analysis of boundary decision
4. Experiments.
5. Summary and Conclusion
Acknowledgment
References

키워드

  • Model selection
  • Linear classifier
  • Classification boundary
  • Overfitting

저자정보

  • Byung Joo Kim School of Computer Engineering Youngsan University, Korea

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