Earticle

초록

영어

Machine learning is ideal for exploiting the opportunities hidden in big data. It delivers on the promise of extracting value from big and disparate data sources with far less reliance on human direction. It is data driven and runs at machine scale. It is well suited to the complexity of dealing with disparate data sources and the huge variety of variables and amounts of data involved. And unlike traditional analysis, machine learning thrives on growing datasets. The more data fed into a machine learning system, the more it can learn and apply the results to higher quality insights. In this paper we propose a robust machine learning approach for dealing with large data set. Through experimental results, proposed method performs well on large data sets.

목차

Abstract
 1. Introduction
 2. Incremental KPCA
  2.1. Eigenspace Updating Criterion
 3. SVM and LS-SVM
  3.1. Support Vector Machine
  3.2. LS -SVM for Big Data 
 4. Experiment 
  4.1. YouTube Comedy Slam Preference Data Set 
  4.2. KDD CUP 99 Data
  4.3. Wine Data
  4.4. NIST Handwritten Data Set
  4.5. Comparison with SVM
 5. Conclusion and Remarks 
 Acknowledgment
 References

키워드

저자

• Byung Joo Kim [ Department of Computer Enginerring Youngsan University, Korea ]

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