Earticle

초록

영어

Classification is to map the data item in the database into a given class. It is an important research direction in data mining. In allusion to the shortcomings of traditional classification methods, such as the decision tree, K nearest neighbor, Bayes , fuzzy logic, genetic algorithms and neural networks and so on, the support vector machine with perfect theory, strong adaptability, global optimization, short training time, good generalization performance is introduced into the classification, a machine learning model based on the SMO algorithm and RBF kernel function of the SVM is proposed to realize a classification method in this paper. This method transforms the nonlinear classification problem into linear classification problem by improving the data dimension. It can better solve the problems of the minimum error in the training set and the larger error in the test set in the traditional algorithm. Application of UCI classification experiment shows that the proposed method takes on the better convergence, faster training speed and higher classification accuracy.

목차

Abstract
 1. Introduction
 2. Support Vector Machine
 3. Machine Learning Model Based on Support Vector Machine
 4. The Key Technology Analysis of Machine Learning Mode
  4.1. The Selection of Kernel Function
  4.2. The SVM Based on SMO Algorithm
 5. Analysis of the Experimental Simulation and Application of Machine Learning Model
  5.1. Classify by Using the SMO Algorithm and Kernel Function
  5.2. Analysis of Large-Scale Data Classification Based on Machine Learning Model
 6. Conclusion
 Acknowledgements
 References

키워드

저자

• Hao Jia [ Department of Electrical Engineering, Dalian Institute of Science and Technology, Dalian 116052 China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Database Theory and Application

• 간기

  격월간

• pISSN

  2005-4270

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605