Earticle

초록

영어

A multiple smooth model is proposed by smoothing technique and piecewise technique for large scale data. Mapping the training data to the hidden space with a hidden function, the proposed model divides the original data into several subclasses by Fuzzy C Means (FCM), whose initial cluster centers are selected by samples with large density indexes; derives the smooth differentiable model by utilizing the entropy function to replace the plus function of the slack vector, and introduces linking rules to combine results of subclasses. Simulations demonstrate that the obtained algorithm maintains good classification accuracies, reduces the training time and hardly varies with kernel parameters.

목차

Abstract
 1. Introduction
 2. MSSVM-FCM: Multiple Smooth Support Vector Machine with FCM Clustering in Hidden Space
  2.1 Hidden Space
  2.2 FCM clustering
  2.3 Multiple Smooth Models in Hidden Space
  2.4 Linking Rule
 3. Experiments and Comparisons
  3.1 Performances Variances with the Class Number
  3.2 Performances Variances with the Kernel Width
  3.3 Performances on Large Scale Data
 4. Conclusions
 Acknowledgment
 References

키워드

저자

• Xian-wei Zhang [ School of Computer Sciences, Xi’an Shiyou University, Xi’an 710065, China ]
• Jinjin Liang [ School of Mathematical Sciences, Xi’an Shiyou University, Xi’an 710065, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Grid and Distributed Computing

• 간기

  격월간

• pISSN

  2005-4262

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605