지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술대회자료
- 저자정보
- 발행연도
- 2013.10
- 수록면
- 539 - 542 (4page)
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초록· 키워드
오류제보하기
In order to overcome some shortages of SVM, an improved classification model is introduced in this paper. For the first problem about isolated points or noises mixed in training data sets which will cause overfitting problem and decrease the capability of generalization for SVM, we proposed modified covering algorithm to find out the isolated points and deal with it by the definition of covering sample density. As for the second problem, time cost for training SVM on large data sets usually is high; we introduce modified CA as the pre-classification step to reduce the training sample scale, by constructing a series of covers and deleting the isolated points, and then use the centroids of the rest covers as the new training data sets for SVM training. By the experiments on the real world data sets, results show the training time can drop significantly, and the accuracy is very close to Lib-SVM. So, CA-SVM is an efficient classification model.
목차
Abstract
1. INTRODUCTION
2. MODIFIED COVERING ALGORITHM
3. SVM BASED ON MODIFIED CA
4. EXPERIMENTS AND RESULTS
5. CONCLUSION
REFERENCES
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