메뉴 건너뛰기




Volumn 156, Issue 6, 2008, Pages 846-861

Maximum patterns in datasets

Author keywords

Classification; Heuristic; Logical analysis of data; Machine learning; Set covering

Indexed keywords

CLASSIFICATION (OF INFORMATION); FORMAL LOGIC; HEURISTIC ALGORITHMS; LEARNING ALGORITHMS; LEARNING SYSTEMS; SOFTWARE PACKAGES;

EID: 39449110119     PISSN: 0166218X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.dam.2007.06.004     Document Type: Article
Times cited : (59)

References (29)
  • 1
    • 31144439712 scopus 로고    scopus 로고
    • Pattern-based clustering and attribute analysis
    • Alexe G., Alexe S., and Hammer P.L. Pattern-based clustering and attribute analysis. Soft Comput. 10 5 (2006) 442-452
    • (2006) Soft Comput. , vol.10 , Issue.5 , pp. 442-452
    • Alexe, G.1    Alexe, S.2    Hammer, P.L.3
  • 2
    • 33644933469 scopus 로고    scopus 로고
    • Spanned patterns in the logical analysis of data
    • Alexe G., and Hammer P.L. Spanned patterns in the logical analysis of data. Discrete Appl. Math. 154 7 (2006) 1039-1049
    • (2006) Discrete Appl. Math. , vol.154 , Issue.7 , pp. 1039-1049
    • Alexe, G.1    Hammer, P.L.2
  • 3
    • 33644957099 scopus 로고    scopus 로고
    • Accelerated algorithm for pattern detection in logical analysis of data
    • Alexe S., and Hammer P.L. Accelerated algorithm for pattern detection in logical analysis of data. Discrete Appl. Math. 154 7 (2006) 1050-1063
    • (2006) Discrete Appl. Math. , vol.154 , Issue.7 , pp. 1050-1063
    • Alexe, S.1    Hammer, P.L.2
  • 5
  • 6
    • 0031334221 scopus 로고    scopus 로고
    • Selection of relevant features and examples in machine learning
    • Blum A.L., and Langley P. Selection of relevant features and examples in machine learning. Artificial Intelligence 97 1-2 (1997) 245-271
    • (1997) Artificial Intelligence , vol.97 , Issue.1-2 , pp. 245-271
    • Blum, A.L.1    Langley, P.2
  • 7
    • 39449128132 scopus 로고    scopus 로고
    • J.C. Borda, Mémoire sur les élections au scrutin, Histoire de l'Academie Royale des Sciences, 1781.
    • J.C. Borda, Mémoire sur les élections au scrutin, Histoire de l'Academie Royale des Sciences, 1781.
  • 9
    • 0030211964 scopus 로고    scopus 로고
    • Bagging predictors
    • Breiman L. Bagging predictors. Mach. Learning 24 2 (1996) 123-140
    • (1996) Mach. Learning , vol.24 , Issue.2 , pp. 123-140
    • Breiman, L.1
  • 10
    • 0042466461 scopus 로고    scopus 로고
    • Learning monotone DNF from a teacher that almost does not answer membership queries
    • Bshouty N.H., and Eiron N. Learning monotone DNF from a teacher that almost does not answer membership queries. J. Mach. Learning Res. 3 1 (2003) 49-57
    • (2003) J. Mach. Learning Res. , vol.3 , Issue.1 , pp. 49-57
    • Bshouty, N.H.1    Eiron, N.2
  • 11
    • 0000301097 scopus 로고
    • A greedy heuristic for the set-covering problem
    • Chvátal V. A greedy heuristic for the set-covering problem. Math. Oper. Res. 4 3 (1979) 233-235
    • (1979) Math. Oper. Res. , vol.4 , Issue.3 , pp. 233-235
    • Chvátal, V.1
  • 12
    • 0001168506 scopus 로고
    • Cause-effect relationships and partially defined Boolean functions
    • Crama Y., Hammer P.L., and Ibaraki T. Cause-effect relationships and partially defined Boolean functions. Ann. Oper. Res. 16 (1988) 299-325
    • (1988) Ann. Oper. Res. , vol.16 , pp. 299-325
    • Crama, Y.1    Hammer, P.L.2    Ibaraki, T.3
  • 13
    • 39449117540 scopus 로고    scopus 로고
    • Dash Optimization, Inc., Xpress-MP Release 2003C: Xpress-Mosel and Xpress-Optimizer, 2003.
    • Dash Optimization, Inc., Xpress-MP Release 2003C: Xpress-Mosel and Xpress-Optimizer, 2003.
  • 14
    • 84976766582 scopus 로고
    • Overfitting and under-computing in machine learning
    • Dietterich T.G. Overfitting and under-computing in machine learning. ACM Comput. Surveys 27 (1995) 326-327
    • (1995) ACM Comput. Surveys , vol.27 , pp. 326-327
    • Dietterich, T.G.1
  • 15
    • 0000259511 scopus 로고    scopus 로고
    • Approximate statistical tests for comparing supervised classification learning algorithms
    • Dietterich T.G. Approximate statistical tests for comparing supervised classification learning algorithms. Neural Comput. 10 7 (1998) 1895-1924
    • (1998) Neural Comput. , vol.10 , Issue.7 , pp. 1895-1924
    • Dietterich, T.G.1
  • 17
    • 0032108328 scopus 로고    scopus 로고
    • A Threshold of ln n for approximating set cover
    • Feige U. A Threshold of ln n for approximating set cover. J. ACM 45 4 (1998) 634-652
    • (1998) J. ACM , vol.45 , Issue.4 , pp. 634-652
    • Feige, U.1
  • 18
    • 58149321460 scopus 로고
    • Boosting a weak learning algorithm by majority
    • Freund Y. Boosting a weak learning algorithm by majority. Inform. Comput. 121 2 (1995) 256-285
    • (1995) Inform. Comput. , vol.121 , Issue.2 , pp. 256-285
    • Freund, Y.1
  • 19
    • 39449132617 scopus 로고    scopus 로고
    • P.L. Hammer, The logic of cause-effect relationships, Lecture at the International Conference on Multi-Attribute Decision Making via Operations Research-based Expert Systems, Passau, Germany, 1986.
    • P.L. Hammer, The logic of cause-effect relationships, Lecture at the International Conference on Multi-Attribute Decision Making via Operations Research-based Expert Systems, Passau, Germany, 1986.
  • 20
    • 0001485441 scopus 로고
    • Approximations of pseudo-Boolean functions; applications to game theory
    • Hammer P.L., and Holzman R. Approximations of pseudo-Boolean functions; applications to game theory. ZOR-Methods Models Oper. Res. 39 (1992) 3-21
    • (1992) ZOR-Methods Models Oper. Res. , vol.39 , pp. 3-21
    • Hammer, P.L.1    Holzman, R.2
  • 21
    • 4544297313 scopus 로고    scopus 로고
    • Pareto-optimal patterns in logical analysis of data
    • Hammer P., Kogan A., Simeone B., and Szedmák S. Pareto-optimal patterns in logical analysis of data. Discrete Appl. Math. 144 1-2 (2004) 79-102
    • (2004) Discrete Appl. Math. , vol.144 , Issue.1-2 , pp. 79-102
    • Hammer, P.1    Kogan, A.2    Simeone, B.3    Szedmák, S.4
  • 23
    • 26944438672 scopus 로고    scopus 로고
    • N. Lavrac, Subgroup Discovery Techniques and Applications, Lecture Notes in Artificial Intelligence, vol. 3518, Springer, Berlin, 2005, pp. 2-14.
    • N. Lavrac, Subgroup Discovery Techniques and Applications, Lecture Notes in Artificial Intelligence, vol. 3518, Springer, Berlin, 2005, pp. 2-14.
  • 27
    • 0032280519 scopus 로고    scopus 로고
    • Boosting the margin: a new explanation for the effectiveness of voting methods
    • Schapire R.E., Freund Y., Bartlett P., and Lee W.S. Boosting the margin: a new explanation for the effectiveness of voting methods. Ann. Statist. 26 5 (1998) 1651-1686
    • (1998) Ann. Statist. , vol.26 , Issue.5 , pp. 1651-1686
    • Schapire, R.E.1    Freund, Y.2    Bartlett, P.3    Lee, W.S.4


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.