Earticle

초록

영어

Advances in digital sensors, communications, computation, and storage have created huge collections of data, capturing information of value to business, science, government, and society. Many machine learning algorithms do not scale beyond data sets of a few million elements or cannot tolerate the statistical noise and gaps found in real-world data. Further research is required to develop algorithms that apply in real-world situations and on data sets of trillions of elements. In this paper we propose a conjugate based novel algorithm to handle the huge collections of data. Through the experimental results, proposed method performs well on huge data from UCI machine learning repository data set.

목차

Abstract
 1. Introduction
 2. Incremental KPCA
  2.1. Eigenspace Updating Criterion
 3. Conjugate LS-SVM for Real World Data
 4. Experiment
  4.1. HIV-1 protease cleavage Data Set
  4.2. TV News Channel Commercial Detection Data Set
  4.3. Gas Sensor Array under Dynamic Gas Mixtures Data Set
 5. Conclusion and Remarks
 References

키워드

저자

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

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