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Volumn 1, Issue , 2006, Pages 389-394

KFOIL: Learning simple relational kernels

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; ARTIFICIAL INTELLIGENCE; INFORMATION ANALYSIS; LOGIC PROGRAMMING;

EID: 33750734364     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (73)

References (24)
  • 1
    • 0004204240 scopus 로고    scopus 로고
    • An assessment of ILP-assisted models for toxicology and the PTE-3 experiment
    • Ashwin Srinivasan, Ross D. King, D. B. 1999. An Assessment of ILP-Assisted Models for Toxicology and the PTE-3 Experiment. In Proc. of ILP'99.
    • (1999) Proc. of ILP'99
    • Srinivasan, A.1    Ross, D.2    King, D.B.3
  • 3
    • 0029407394 scopus 로고
    • Applications of inductive logic programming
    • Bratko, I., and Muggleton, S. 1995. Applications of Inductive Logic Programming. Comm. of the ACM 38(11):65-70.
    • (1995) Comm. of the ACM , vol.38 , Issue.11 , pp. 65-70
    • Bratko, I.1    Muggleton, S.2
  • 5
    • 38049157771 scopus 로고    scopus 로고
    • Condensed representations for inductive logic programming
    • De Raedt, L., and Ramon, J. 2004. Condensed Representations for Inductive Logic Programming. In Proc. of KR'04.
    • (2004) Proc. of KR'04
    • De Raedt, L.1    Ramon, J.2
  • 6
    • 84880088183 scopus 로고    scopus 로고
    • Finding frequent substructures in chemical compounds
    • Dehaspe, L.; Toivonen, H.; and King, R. 1998. Finding Frequent Substructures in Chemical Compounds. In Proc. of KDD'98.
    • (1998) Proc. of KDD'98
    • Dehaspe, L.1    Toivonen, H.2    King, R.3
  • 8
    • 4444288656 scopus 로고    scopus 로고
    • Kernels and distances for structured data
    • Gaertner, T.; Lloyd, J.; and Flach, P. 2004. Kernels and Distances for Structured Data. Machine Learning 57(3):205-232.
    • (2004) Machine Learning , vol.57 , Issue.3 , pp. 205-232
    • Gaertner, T.1    Lloyd, J.2    Flach, P.3
  • 9
    • 4444231365 scopus 로고    scopus 로고
    • A survey of kernels for structured data
    • Gaertner, T. 2003. A Survey of Kernels for Structured Data. SIGKDD Explorations 5(1):49-58.
    • (2003) SIGKDD Explorations , vol.5 , Issue.1 , pp. 49-58
    • Gaertner, T.1
  • 10
    • 0000183134 scopus 로고
    • Relating chemical activity to structure: An examination of ILP successes
    • King, R.; Srinivasan, A.; and Sternberg, M. 1995. Relating Chemical Activity to Structure: an Examination of ILP Successes. New Generation Computing 13(2,4):411-433.
    • (1995) New Generation Computing , vol.13 , Issue.2-4 , pp. 411-433
    • King, R.1    Srinivasan, A.2    Sternberg, M.3
  • 11
    • 4444283088 scopus 로고    scopus 로고
    • Distance based approaches to relational learning and clustering
    • Springer
    • Kirsten, M.; Wrobel, S.; and Horváth, T. 2001. Distance based approaches to relational learning and clustering. In Relational Data Mining, 213-230. Springer.
    • (2001) Relational Data Mining , pp. 213-230
    • Kirsten, M.1    Wrobel, S.2    Horváth, T.3
  • 12
    • 0031381525 scopus 로고    scopus 로고
    • Wrappers for feature subset selection
    • Kohavi, R., and John, G. 1997. Wrappers for feature subset selection. Art. Int. 97(1-2):273-324.
    • (1997) Art. Int. , vol.97 , Issue.1-2 , pp. 273-324
    • Kohavi, R.1    John, G.2
  • 13
    • 0030349752 scopus 로고    scopus 로고
    • Structural regression trees
    • Kramer, S. 1996. Structural Regression Trees. In Proc. of AAAI, 812-819.
    • (1996) Proc. of AAAI , pp. 812-819
    • Kramer, S.1
  • 15
  • 16
    • 23244434257 scopus 로고    scopus 로고
    • Learning the kernel function via regularization
    • Micchelli, C. A., and Pontil, M. 2005. Learning the Kernel Function via Regularization. J. Mach. Learn. Res. 6:1099-1125.
    • (2005) J. Mach. Learn. Res. , vol.6 , pp. 1099-1125
    • Micchelli, C.A.1    Pontil, M.2
  • 17
    • 33646363498 scopus 로고    scopus 로고
    • Support vector inductive logic programming
    • Muggleton, S.; Amini, A.; and Sternberg, M. 2005. Support Vector Inductive Logic Programming. In Proc. of DS'05, 163-175.
    • (2005) Proc. of DS'05 , pp. 163-175
    • Muggleton, S.1    Amini, A.2    Sternberg, M.3
  • 19
    • 33646340423 scopus 로고    scopus 로고
    • Kernels on prolog proof trees: Statistical learning in the ILP setting
    • Passerini, A.; Frasconi, P.; and De Raedt, L. 2006. Kernels on prolog proof trees: Statistical learning in the ILP setting. J. Mach. Learn. Res. 7:307-342.
    • (2006) J. Mach. Learn. Res. , vol.7 , pp. 307-342
    • Passerini, A.1    Frasconi, P.2    De Raedt, L.3
  • 21
    • 0001172265 scopus 로고
    • Learning logical definitions from relations
    • Quinlan, J. 1990. Learning Logical Definitions from Relations. Machine Learning 5:239-266.
    • (1990) Machine Learning , vol.5 , pp. 239-266
    • Quinlan, J.1
  • 22
    • 84947431598 scopus 로고    scopus 로고
    • A framework for defining distances between first-order logic objects
    • Ramon, J., and Bruynooghe, M. 1998. A Framework for Defining Distances Between First-Order Logic Objects. In Proc. of ILP, 271-280.
    • (1998) Proc. of ILP , pp. 271-280
    • Ramon, J.1    Bruynooghe, M.2
  • 23
    • 84890445089 scopus 로고    scopus 로고
    • Overfitting in making comparisons between variable selection methods
    • Reunanen, J. 2003. Overfitting in making comparisons between variable selection methods. J. Mach. Learn. Res. 3:1371-1382.
    • (2003) J. Mach. Learn. Res. , vol.3 , pp. 1371-1382
    • Reunanen, J.1
  • 24
    • 0030212927 scopus 로고    scopus 로고
    • Theories for mutagenicity: A study of first-order and feature-based induction
    • Srinivasan, A.; Muggleton, S.; King, R.; and Sternberg, M. 1996. Theories for Mutagenicity: a Study of First-Order and Feature-Based Induction. Art. Int. 85:277-299.
    • (1996) Art. Int. , vol.85 , pp. 277-299
    • Srinivasan, A.1    Muggleton, S.2    King, R.3    Sternberg, M.4


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.