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Volumn , Issue , 2009, Pages 2025-2028

Feature selection for ranking using boosted trees

Author keywords

Boosted trees; Feature selection; Information retrieval

Indexed keywords

BACKWARD ELIMINATION; BOOSTED REGRESSION TREES; BOOSTED TREES; FEATURE COMPUTATION; FEATURE MODELS; FEATURE SELECTION; FEATURE SELECTION METHODS; FEATURE SIMILARITIES; MODELING PARAMETERS; RANDOMIZED APPROACH; RANKING FUNCTIONS; RELATIVE IMPORTANCE;

EID: 74549171478     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1645953.1646292     Document Type: Conference Paper
Times cited : (59)

References (13)
  • 1
    • 0035470889 scopus 로고    scopus 로고
    • Greedy Function Approximation: A Gradient Boosting Machine
    • J. H. Friedman. 2001. Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics 29(5):1189-1232.
    • (2001) Annals of Statistics , vol.29 , Issue.5 , pp. 1189-1232
    • Friedman, J.H.1
  • 3
    • 0031538203 scopus 로고    scopus 로고
    • S. Robertson. Overview of the okapi projects. 1997. Journal of Documentation, 53(1): 3-7.
    • S. Robertson. Overview of the okapi projects. 1997. Journal of Documentation, 53(1): 3-7.
  • 9
    • 37748998960 scopus 로고    scopus 로고
    • Learning to Rank Using Classification and Gradient Boosting
    • MSR-TR-2007-74
    • P. Li, C. J. C. Burges and Q. Wu. 2007. Learning to Rank Using Classification and Gradient Boosting. Microsoft Research Technical Report MSR-TR-2007-74.
    • (2007) Microsoft Research Technical Report
    • Li, P.1    Burges, C.J.C.2    Wu, Q.3
  • 11


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.