Earticle

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

키워드

저자

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

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  The International Journal of Advanced Smart Convergence

• 간기

  계간

• pISSN

  2288-2847

• eISSN

  2288-2855

• 수록기간

  2012~2025

• 십진분류

  KDC 326 DDC 380