Earticle

초록

영어

Developing a machine learning model and putting it into production goes through a number of steps. Automated Machine Learning(AutoML) appeared to increase productivity and efficiency by automating inefficient tasks that occur while repeating this process whenever machine learning is applied. The high degree of automation of AutoML models allows non-experts to use machine learning models and techniques without the need to become machine learning experts. Automating the process of applying machine learning end-to-end with AutoML models has the added benefit of creating simpler solutions, generating these solutions faster, and often generating models that outperform hand-designed models. In this paper, the AutoML data is collected and AutoML's Color Petri net model is created and analyzed based on it.

목차

Abstract
1. INTRODUCTION
2. AUTOMATED MACHINE LEARNING
3. AUTOML MODEL
3.1 AutoML Model
3.2 Modeling of AutoML Model using CPN Tools
4. ANALYSIS of AUTOML MODEL
4.1 Modeling of AutoML Model using CPN Tools
4.2 Analysis of Proposed Model using the Occurrence Graph
5. CONCLUSION
ACKNOWLEDGEMENT
REFERENCES

키워드

저자

• Yo-Seob Lee [ Professor, School of ICT Convergence, Pyeongtaek University ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Advanced Culture Technology(IJACT)

• 간기

  계간

• pISSN

  2288-7202

• eISSN

  2288-7318

• 수록기간

  2013~2025

• 등재여부

  KCI 등재

• 십진분류

  KDC 600 DDC 700