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Volumn 5351 LNAI, Issue , 2008, Pages 485-496

Learning from the past with experiment databases

Author keywords

Data analysis; Machine learning; Meta learning

Indexed keywords

ARTIFICIAL INTELLIGENCE; BIONICS; DATABASE SYSTEMS; EDUCATION; EXPERIMENTS; LEARNING SYSTEMS; RESEARCH; ROBOT LEARNING;

EID: 58349112632     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-89197-0_45     Document Type: Conference Paper
Times cited : (10)

References (12)
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    • 38049132711 scopus 로고    scopus 로고
    • Blockeel, H., Vanschoren, J.: Experiment databases: Towards an improved experimental methodology in machine learning. In: Kok, J.N., Koronacki, J., Lopez de Mantaras, R., Matwin, S., Mladenič, D., Skowron, A. (eds.) PKDD 2007. LNCS (LNAI), 4702, pp. 6-17. Springer, Heidelberg (2007)
    • Blockeel, H., Vanschoren, J.: Experiment databases: Towards an improved experimental methodology in machine learning. In: Kok, J.N., Koronacki, J., Lopez de Mantaras, R., Matwin, S., Mladenič, D., Skowron, A. (eds.) PKDD 2007. LNCS (LNAI), vol. 4702, pp. 6-17. Springer, Heidelberg (2007)
  • 2
    • 84864854508 scopus 로고    scopus 로고
    • Brain, D., Webb, G.: The Need for Low Bias Algorithms in Classification Learning from Large Data Sets. In: Elomaa, T., Mannila, H., Toivonen, H. (eds.) PKDD 2002. LNCS (LNAI), 2431, pp. 62-73. Springer, Heidelberg (2002)
    • Brain, D., Webb, G.: The Need for Low Bias Algorithms in Classification Learning from Large Data Sets. In: Elomaa, T., Mannila, H., Toivonen, H. (eds.) PKDD 2002. LNCS (LNAI), vol. 2431, pp. 62-73. Springer, Heidelberg (2002)
  • 4
    • 0004060921 scopus 로고    scopus 로고
    • Correlation-based Feature Selection for Machine Learning. Ph.D diss
    • Hamilton, NZ: Waikato University, Department of Computer Science 1998
    • Hall, M.A.: Correlation-based Feature Selection for Machine Learning. Ph.D diss. Hamilton, NZ: Waikato University, Department of Computer Science (1998)
    • Hall, M.A.1
  • 5
    • 0027580356 scopus 로고
    • Very simple classification rules perform well on most commonly used datasets
    • Holte, R.: Very simple classification rules perform well on most commonly used datasets. Machine Learning 11, 63-91 (1993)
    • (1993) Machine Learning , vol.11 , pp. 63-91
    • Holte, R.1
  • 6
    • 0033705380 scopus 로고    scopus 로고
    • Building Algorithm Profiles for prior Model Selection in Knowledge Discovery Systems
    • Kalousis, A., Hilario, M.: Building Algorithm Profiles for prior Model Selection in Knowledge Discovery Systems. Engineering Intelligent Systems 8(2) (2000)
    • (2000) Engineering Intelligent Systems , vol.8 , Issue.2
    • Kalousis, A.1    Hilario, M.2
  • 7
    • 84949799605 scopus 로고    scopus 로고
    • Peng, Y., et al.: Improved Dataset Characterisation for Meta-Learning. In: Lange, S., Satoh, K., Smith, C.H. (eds.) DS 2002. LNCS, 2534, pp. 141-152. Springer, Heidelberg (2002)
    • Peng, Y., et al.: Improved Dataset Characterisation for Meta-Learning. In: Lange, S., Satoh, K., Smith, C.H. (eds.) DS 2002. LNCS, vol. 2534, pp. 141-152. Springer, Heidelberg (2002)
  • 9
    • 84867758442 scopus 로고    scopus 로고
    • Van Someren, M.: Model Class Selection and Construction: Beyond the Procrustean Approach to Machine Learning Applications. In: Paliouras, G., Karkaletsis, V., Spyropoulos, C.D. (eds.) ACAI 1999. LNCS (LNAI), 2049, pp. 196-217. Springer, Heidelberg (2001)
    • Van Someren, M.: Model Class Selection and Construction: Beyond the Procrustean Approach to Machine Learning Applications. In: Paliouras, G., Karkaletsis, V., Spyropoulos, C.D. (eds.) ACAI 1999. LNCS (LNAI), vol. 2049, pp. 196-217. Springer, Heidelberg (2001)


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.