메뉴 건너뛰기




Volumn 1910, Issue , 2000, Pages 440-445

Observational logic integrates data mining based on statistics and neural networks

Author keywords

[No Author keywords available]

Indexed keywords

CALCULATIONS; COMPUTER CIRCUITS; NEURAL NETWORKS;

EID: 84974695520     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-45372-5_48     Document Type: Conference Paper
Times cited : (4)

References (21)
  • 1
    • 0029484103 scopus 로고
    • Survey and critique of techniques for extracting rules from trained artificical neural networks
    • R. Andrews, J. Diederich, and A.B. Tickle. Survey and critique of techniques for extracting rules from trained artificical neural networks. Knowledge Based Systems, 8: 378-389, 1995.
    • (1995) Knowledge Based Systems , vol.8 , pp. 378-389
    • Andrews, R.1    Diederich, J.2    Tickle, A.B.3
  • 5
    • 84958950191 scopus 로고    scopus 로고
    • Data mining using dynamically constructed recurrent fuzzy neural networks
    • Springer-Verlag, Berlin
    • Y. Frayman and L. Wang. Data mining using dynamically constructed recurrent fuzzy neural networks. In Research and Development in Knowledge Discovery and Data Mining, pages 122-131. Springer-Verlag, Berlin, 1998.
    • (1998) Research and Development in Knowledge Discovery and Data Mining , pp. 122-131
    • Frayman, Y.1    Wang, L.2
  • 8
    • 84949188149 scopus 로고    scopus 로고
    • Formal logics of discovery and hypothesis formation by machine
    • Springer-Verlag, Tokyo
    • P. Hájek and M. Holeňa. Formal logics of discovery and hypothesis formation by machine. In Discovery Science, pages 291-302. Springer-Verlag, Tokyo, 1998.
    • (1998) Discovery Science , pp. 291-302
    • Hájek, P.1    Holeňa, M.2
  • 10
    • 0031140465 scopus 로고    scopus 로고
    • Acquiring rule sets as a product of learning in a logical neural architecture
    • M.J. Healy and T.P. Caudell. Acquiring rule sets as a product of learning in a logical neural architecture. IEEE Transactions on Neural Networks, 8: 461-474, 1997.
    • (1997) IEEE Transactions on Neural Networks , vol.8 , pp. 461-474
    • Healy, M.J.1    Caudell, T.P.2
  • 11
    • 0002647748 scopus 로고    scopus 로고
    • Exploratory data processing using a fuzzy generalization of the Guha approach
    • John Wiley and Sons, New York
    • M. Holeňa. Exploratory data processing using a fuzzy generalization of the Guha approach. In Fuzzy Logic, pages 213-229. John Wiley and Sons, New York, 1996.
    • (1996) Fuzzy Logic , pp. 213-229
    • Holeňa, M.1
  • 12
    • 0001181941 scopus 로고    scopus 로고
    • Fuzzy hypotheses for Guha implications
    • M. Holeňa. Fuzzy hypotheses for Guha implications. Fuzzy Sets and Systems, 98: 101-125, 1998.
    • (1998) Fuzzy Sets and Systems , vol.98 , pp. 101-125
    • Holeňa, M.1
  • 14
    • 0012374713 scopus 로고    scopus 로고
    • Statistical, logic-based, and neural networks based methods for mining rules from data
    • NATO Science Series Publishers, to appear
    • M. Holeňa. Statistical, logic-based, and neural networks based methods for mining rules from data. In Multisensor and Sensor Data Fusion. NATO Science Series Publishers, to appear.
    • Multisensor and Sensor Data Fusion
    • Holeňa, M.1
  • 16
    • 0012440227 scopus 로고    scopus 로고
    • Logical calculi for knowledge discovery in databases
    • Springer-Verlag, Berlin
    • J. Rauch. Logical calculi for knowledge discovery in databases. In Principles of Data Mining and Knowledge Discovery, pages 47-57. Springer-Verlag, Berlin, 1997.
    • (1997) Principles of Data Mining and Knowledge Discovery , pp. 47-57
    • Rauch, J.1
  • 18
    • 0030631792 scopus 로고    scopus 로고
    • Extracting rules from neural networks by pruning and hidden unit splitting
    • R. Setiono. Extracting rules from neural networks by pruning and hidden unit splitting. Neural Computation, 9: 205-225, 1997.
    • (1997) Neural Computation , vol.9 , pp. 205-225
    • Setiono, R.1
  • 19
    • 0032208720 scopus 로고    scopus 로고
    • The truth is there: Directions and challenges in extracting rules from trained artificial neural networks
    • A.B. Tickle, R. Andrews, M. Golea, and J. Diederich. The truth is there: Directions and challenges in extracting rules from trained artificial neural networks. IEEE Transactions on Neural Networks, 9: 1058-1068, 1998.
    • (1998) IEEE Transactions on Neural Networks , vol.9 , pp. 1058-1068
    • Tickle, A.B.1    Andrews, R.2    Golea, M.3    Diederich, J.4
  • 20
    • 0027678679 scopus 로고
    • Extracting refined rules from knowledge-based neural networks
    • G.G. Towell and J.W. Shavlik. Extracting refined rules from knowledge-based neural networks. Machine Learning, 13: 71-101, 1993.
    • (1993) Machine Learning , vol.13 , pp. 71-101
    • Towell, G.G.1    Shavlik, J.W.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.