메뉴 건너뛰기




Volumn 1, Issue , 2010, Pages 291-296

Learning in presence of ontology mapping errors

Author keywords

Nasty classification noise; Ontology mapping errors; PAC; Semantically disparate data sources

Indexed keywords

DATA SOURCE; MAPPING ERROR; NASTY CLASSIFICATION NOISE; ONTOLOGY MAPPING; ONTOLOGY MAPPING ERRORS; PAC; PREDICTIVE MODELS; THEORETICAL RESULT;

EID: 78649862694     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/WI-IAT.2010.138     Document Type: Conference Paper
Times cited : (1)

References (24)
  • 1
    • 0036036947 scopus 로고    scopus 로고
    • Data integration: A theoretical perspective
    • M. Lenzerini, "Data integration: a theoretical perspective," in Proceedings of PODS '02, pp. 233-246.
    • Proceedings of PODS '02 , pp. 233-246
    • Lenzerini, M.1
  • 2
    • 0030679824 scopus 로고    scopus 로고
    • Managing semantic heterogeneity in databases: A theoretical prospective
    • R. Hull, "Managing semantic heterogeneity in databases: a theoretical prospective," in Proceedings of PODS '97, pp. 51-61.
    • Proceedings of PODS '97 , pp. 51-61
    • Hull, R.1
  • 3
    • 0042091196 scopus 로고    scopus 로고
    • Ontology mapping: The state of the art
    • Y. Kalfoglou and M. Schorlemmer, "Ontology mapping: The state of the art," Knowl. Eng. Rev, vol. 18, no. 04391, pp. 1-31, 2005.
    • (2005) Knowl. Eng. Rev , vol.18 , Issue.4391 , pp. 1-31
    • Kalfoglou, Y.1    Schorlemmer, M.2
  • 4
    • 0021518106 scopus 로고
    • A theory of the learnable
    • L. G. Valiant, "A theory of the learnable," Commun. ACM, vol. 27, no. 11, pp. 1134-1142, 1984.
    • (1984) Commun. ACM , vol.27 , Issue.11 , pp. 1134-1142
    • Valiant, L.G.1
  • 7
    • 33747739087 scopus 로고    scopus 로고
    • Robust decision trees: Removing outliers from databases
    • G. H. John, "Robust decision trees: Removing outliers from databases," in Proceedings of KDD '95, pp. 174-179.
    • Proceedings of KDD '95 , pp. 174-179
    • John, G.H.1
  • 8
    • 0342313951 scopus 로고    scopus 로고
    • Pessimistic decision tree pruning based on tree size
    • Y. Mansour, "Pessimistic decision tree pruning based on tree size," in Proceedings of ICML '97, pp. 195-201.
    • Proceedings of ICML '97 , pp. 195-201
    • Mansour, Y.1
  • 10
    • 33746071857 scopus 로고    scopus 로고
    • Ensemble methods for noise elimination in classification problems
    • S. Verbaeten and A. V. Assche, "Ensemble methods for noise elimination in classification problems," in Multiple Classifier Systems, 2003, pp. 317-325.
    • (2003) Multiple Classifier Systems , pp. 317-325
    • Verbaeten, S.1    Assche, A.V.2
  • 11
    • 1942484424 scopus 로고    scopus 로고
    • Eliminating class noise in large datasets
    • X. Zhu, X. Wu, and Q. Chen, "Eliminating class noise in large datasets," in ICML, 2003, pp. 920-927.
    • (2003) ICML , pp. 920-927
    • Zhu, X.1    Wu, X.2    Chen, Q.3
  • 13
    • 0034250160 scopus 로고    scopus 로고
    • An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization
    • T. G. Dietterich, "An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization," Mach. Learn., vol. 40, no. 2, pp. 139-157, 2000.
    • (2000) Mach. Learn. , vol.40 , Issue.2 , pp. 139-157
    • Dietterich, T.G.1
  • 15
    • 0024750852 scopus 로고
    • Learnability and the VapnikChervonenkis dimension
    • "Learnability and the VapnikChervonenkis dimension," Journal of the ACM, vol. 36, no. 4, pp. 929-865, 1989.
    • (1989) Journal of the ACM , vol.36 , Issue.4 , pp. 929-865
  • 18
    • 33646515517 scopus 로고    scopus 로고
    • Algorithms and software for collaborative discovery from autonomous, semantically heterogeneous information sources (invited paper)
    • D. Caragea, J. Zhang, J. Bao, J. Pathak, and V. Honavar, "Algorithms and software for collaborative discovery from autonomous, semantically heterogeneous information sources (invited paper)," in Proceedings of ALT 05, pp. 13-44.
    • Proceedings of ALT 05 , pp. 13-44
    • Caragea, D.1    Zhang, J.2    Bao, J.3    Pathak, J.4    Honavar, V.5
  • 20
    • 0242709373 scopus 로고    scopus 로고
    • A theoretical framework for learning from a pool of disparate data sources
    • S. Ben-david, J. Gehrke, and R. Schuller, "A theoretical framework for learning from a pool of disparate data sources," in Proceedings of KDD 2002, pp. 443-449.
    • (2002) Proceedings of KDD , pp. 443-449
    • Ben-David, S.1    Gehrke, J.2    Schuller, R.3
  • 21
    • 0000492326 scopus 로고    scopus 로고
    • Learning from noisy examples
    • April
    • D. Angluin and P. Laird, "Learning from noisy examples," Machine Learning, vol. 2, pp. 343-370, April 1998.
    • (1998) Machine Learning , vol.2 , pp. 343-370
    • Angluin, D.1    Laird, P.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.