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Improvement of the Convergence Rate of Deep Learning by Using Scaling Method

원문정보

초록

영어

Deep learning neural network becomes very popular nowadays due to the reason that it can learn a very complex dataset such as the image dataset. Although deep learning neural network can produce high accuracy on the image dataset, it needs a lot of time to reach the convergence stage. To solve the issue, we have proposed a scaling method to improve the neural network to achieve the convergence stage in a shorter time than the original method. From the result, we can observe that our algorithm has higher performance than the other previous work.

목차

Abstract
 1. Introduction
 2. Scaled neural network
  2.1 Step to produce the k
 3. Experimental method
 4. Experimental results
 5. Conclusion
 Acknowledgement
 References

저자정보

  • Jiacang Ho Department of Ubiquitous IT, Graduate School, Dongseo University
  • Dae-Ki Kang Department of Computer Engineering, Dongseo University

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