메뉴 건너뛰기




Volumn 26, Issue 1, 1997, Pages 5-23

Empirical support for winnow and weighted-majority algorithms: Results on a calendar scheduling domain

Author keywords

Multiplicative algorithms; Weighted majority; Winnow

Indexed keywords

CONVERGENCE OF NUMERICAL METHODS; LEARNING SYSTEMS; PERFORMANCE; SCHEDULING; SIMULATION;

EID: 0030819669     PISSN: 08856125     EISSN: None     Source Type: Journal    
DOI: 10.1023/A:1007335615132     Document Type: Article
Times cited : (157)

References (14)
  • 2
    • 0007563290 scopus 로고
    • Learning boolean functions in an infinite attribute space
    • Blum, A. (1992). Learning boolean functions in an infinite attribute space. Machine Learning, 9:373-386.
    • (1992) Machine Learning , vol.9 , pp. 373-386
    • Blum, A.1
  • 9
    • 1642535723 scopus 로고
    • Interfaces that learn: A learning apprentice for calendar management
    • Carnegie Mellon University
    • Jourdan, J., Dent, L., McDermott, J., & Zabowski, D. (1991). Interfaces that learn: A learning apprentice for calendar management. Technical Report CMU-CS-91-135, Carnegie Mellon University.
    • (1991) Technical Report , vol.CMU-CS-91-135
    • Jourdan, J.1    Dent, L.2    McDermott, J.3    Zabowski, D.4
  • 10
    • 34250091945 scopus 로고
    • Learning quickly when irrelevant attributes abound: A new linear-threshold algorithm
    • Littlestone, N. (1988). Learning quickly when irrelevant attributes abound: A new linear-threshold algorithm. Machine Learning, 2:285-318.
    • (1988) Machine Learning , vol.2 , pp. 285-318
    • Littlestone, N.1
  • 12
    • 0000511449 scopus 로고
    • Redundant noisy attributes, attribute errors, and linear-threshold learning using winnow
    • Santa Cruz, California. Morgan Kaufmann
    • Littlestone, N. (1991). Redundant noisy attributes, attribute errors, and linear-threshold learning using winnow. In Proceedings of the Fourth Annual Workshop on Computational Learning Theory, pages 147-156, Santa Cruz, California. Morgan Kaufmann.
    • (1991) Proceedings of the Fourth Annual Workshop on Computational Learning Theory
    • Littlestone, N.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.