• 검색어에 아래의 연산자를 사용하시면 더 정확한 검색결과를 얻을 수 있습니다.
  • 검색연산자
검색연산자 기능 검색시 예
() 우선순위가 가장 높은 연산자 예1) (나노 (기계 | machine))
공백 두 개의 검색어(식)을 모두 포함하고 있는 문서 검색 예1) (나노 기계)
예2) 나노 장영실
| 두 개의 검색어(식) 중 하나 이상 포함하고 있는 문서 검색 예1) (줄기세포 | 면역)
예2) 줄기세포 | 장영실
! NOT 이후에 있는 검색어가 포함된 문서는 제외 예1) (황금 !백금)
예2) !image
* 검색어의 *란에 0개 이상의 임의의 문자가 포함된 문서 검색 예) semi*
"" 따옴표 내의 구문과 완전히 일치하는 문서만 검색 예) "Transform and Quantization"
쳇봇 이모티콘
ScienceON 챗봇입니다.
궁금한 것은 저에게 물어봐주세요.

논문 상세정보

미분류 데이터의 초기예측을 통한 군집기반의 부분지도 학습방법

A Clustering-based Semi-Supervised Learning through Initial Prediction of Unlabeled Data


Semi-supervised learning uses a small amount of labeled data to predict labels of unlabeled data as well as to improve clustering performance, whereas unsupervised learning analyzes only unlabeled data for clustering purpose. We propose a new clustering-based semi-supervised learning method by reflecting the initial predicted labels of unlabeled data on the objective function. The initial prediction should be done in terms of a discrete probability distribution through a classification method using labeled data. As a result, clusters are formed and labels of unlabeled data are predicted according to the Information of labeled data in the same cluster. We evaluate and compare the performance of the proposed method in terms of classification errors through numerical experiments with blinded labeled data.

참고문헌 (16)

  1. Bar-Hillel, A., T. hertz, N. Shental, and D. Weinshall, Learning distance functions using equivalence relations. Proceedings of 20th International Conference on Machine Learning, Washington, USA, 2003, pp.11-18. 
  2. Bilenko, M., S. Basu, and R. Mooney, Integrating constraints and metric learning in semisupervised clustering. Proceedings of the 21st International Conference on Machine Learning, Banff, Canada, 2004, pp.81-88. 
  3. Tan, P.N., M. Steinbach, and V.Kumar, Introduction to Data Mining, Pearson Education, Boston, 2006. 
  4. Demiriz, A., K. Bennett, and M. Embrechts, Semi-Supervised clustering using genetic algorithms. Intelligent Engineering Systems, Vol.9(1999), pp.809-814. 
  5. Xing, E.P., A.Y. Ng, M.I. Jordan, and S. Russell, Distance metric learning, with application to clustering with side information. Advances in Neural Information Processing Systems, Vol. 15(2003), pp.505-512. 
  6. Bouchachia, A. and W. pedrycz, Data clustering with partial supervision. Data Mining and Knowledge Discovery, Vol.12, No.1(2006), pp. 47-78. 
  7. Chapelle, O. and A. Zien, Semi-supervised classification by low density separation, Proceedings of the 10th International Workshop on Artificial Intelligence and Statistics, 2005, pp. 57-64. 
  8. Klein, D., S.D. Kamvar, and C. Manning, From instance-level constraints to space-level constraints : Making the most of prior knowledge in data clustering. Proceedings of the 19th International Conference on Machine Learning, 2002, pp.307-314. 
  9. Dempster, A.P., N.M. Laird, and D.B. Rubin, Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society B, Vol.39(1977), pp.1-38. 
  10. Wagstaff, K., C. Cardie, S. Rogers, and S. Schroedl, Constrained K-means clustering with background knowledge. Proceedings of the 18th International Conference on Machine Learning, Massachusetts, USA, 2001, pp.577-584. 
  11. Zhu, X.Semi-supervised learning literature survey, Computer Sciences TR 1530, University of Wisconsin-Madison. http://www.cs.wisc. edu/-jerryzhu/pub/s sl_survey.pdf, 2007. 
  12. Cozman, F., I. Cohen, and M. Cirelo, Semi- Supervised learning of mixture models. Proceedings of the 20th International Conference on Machine Learning, 2003, pp.99-106. 
  13. Lee, D. and J. Lee, Equilibrium-based support vector machine for semi-supervised classification, IEEE Trans. on Neural Networks, Vol.18, No.2(2007), pp.578-583. 
  14. Nigam, K., A. McCallum, S. Thrun, and T. Mitchell, Text classification from labeled and unlabeled documents using EM, Machine Learning, Vol.39(2000), pp.103-134. 
  15. Basu, S., A. Banerjee, and R. Mooney, Semisupervised clustering by seeding. Proceedings of the 19th International Conference on Machine Learning, Sydney, Australia, 2002, pp. 19-26. 
  16. UCI repository : http://www.ics.uci.edu/-mlearn/MLRepository .html. 

이 논문을 인용한 문헌 (0)

  1. 이 논문을 인용한 문헌 없음


원문 PDF 다운로드

  • ScienceON :
  • KCI :

원문 URL 링크

원문 PDF 파일 및 링크정보가 존재하지 않을 경우 KISTI DDS 시스템에서 제공하는 원문복사서비스를 사용할 수 있습니다. (원문복사서비스 안내 바로 가기)

상세조회 0건 원문조회 0건

DOI 인용 스타일