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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 한국자료분석학회 Journal of The Korean Data Analysis Society Journal of The Korean Data Analysis Society 제7권 제2호
- 발행연도
- 2005.1
- 수록면
- 411 - 417 (7page)
이용수
초록· 키워드
오류제보하기
Least squares support vector machine(LS-SVM) is a well known and useful machine learning ways for statistical classification and regression analysis. LS-SVM can be a good substitute for traditional statistical method but computational difficulties are still remained to operate the inversion of matrix of huge data set. In modern information society, we can easily obtain a large data sets by on-line or batch mode. For the analysis of these kind of huge data sets, we suggest an on-line pruning regression method based on LS-SVM. With relatively small number of pruned support vectors, we can have almost same performance as regression with full data set.
목차
등록된 정보가 없습니다.
참고문헌 (10)
참고문헌 신청
/ 1909 / Functions of Positive and Negative Type and Their Connection with Theory of Integral Equations a : 415 ~ 446
/ 1995 / The Nature of Statistical Learning Theory
/ 1998 / On a Kernel-Based Method for Pattern Recognitionpproximation and Operator Inversion Vol. 22 : 211 ~ 231
/ 1998 / Statistical Learning Theory,
/ 1999 / Least Square Support Vector Machine Classifier : 293 ~ 300
/ 1995 / The Nature of Statistical Learning Theory
/ 1998 / On a Kernel-Based Method for Pattern Recognitionpproximation and Operator Inversion Vol. 22 : 211 ~ 231
/ 1998 / Statistical Learning Theory,
/ 1999 / Least Square Support Vector Machine Classifier : 293 ~ 300
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