Earticle

초록

영어

Machine learning is the core of artificial intelligence. It is a fundamental way to the computer intelligence. Support vector machine is one of the important methods in the field of machine learning. It has the advantages of global optimization and strong generalization ability. It has been successfully applied to face recognition, fault diagnosis, financial forecasting and other fields. In this paper, a novel SVR model is proposed to forecast GDP. In the model, The neighborhood rough set (NRS) is used to reduce the index set and the chaotic genetic algorithm (CGA) is adopted to parameters searching in SVR model. Then the novel model NRS-CGA-SVR is established to predict GDP of Anhui province. The results show that the proposed model is better than the other models presented in this paper on forecasting GDP.

목차

Abstract
 1. Introduction
 2. Machine Learning
 3. The optimized SVR model
  3.1. The principle of Standard SVR Model
  3.2. The Principle of Neighborhood Rough Set
  3.3. The Chaotic Genetic Algorithm
 4. Model Construct and Prediction
 5. Conclusion
 References

키워드

저자

• Zhikun Xu [ College of Finance, Hebei Normal University of Science and Technology, Qinhuangdao, Hebei, P.R.China, 066004 ]
• Yabin Gao [ College of Finance, Hebei Normal University of Science and Technology, Qinhuangdao, Hebei, P.R.China, 066004 ]
• Yingying Jin [ College of Finance, Hebei Normal University of Science and Technology, Qinhuangdao, Hebei, P.R.China, 066004 ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Multimedia and Ubiquitous Engineering

• 간기

  월간

• pISSN

  1975-0080

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605