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Volumn , Issue , 2008, Pages 313-318

Learning a metric for music similarity

Author keywords

[No Author keywords available]

Indexed keywords

CONTENT-BASED FEATURES; DISTANCE-BASED; EMBEDDINGS; EUCLIDEAN DISTANCE; EUCLIDEAN METRICS; FEATURE SPACE; LINEAR TRANSFORM; MUSIC SIMILARITY; NEAREST-NEIGHBORS;

EID: 84873426251     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (80)

References (11)
  • 4
    • 84873424784 scopus 로고    scopus 로고
    • downloaded March 31
    • The Echo Nest Analyze API. http://developer.echonest.com/docs/analyze/ xmlxmlde-scription, downloaded March 31, 2008.
    • (2008) The Echo Nest Analyze API
  • 5
    • 0027580356 scopus 로고
    • Very simple classification rules perform well on most commonly used datasets
    • R. Holte. Very simple classification rules perform well on most commonly used datasets. Mach. Learn., 11 (1), pp. 63-90, 1993.
    • (1993) Mach. Learn , vol.11 , Issue.1 , pp. 63-90
    • Holte, R.1
  • 10
    • 33749257955 scopus 로고    scopus 로고
    • Distance metric learning for large margin nearest-neighbor classification
    • Vancouver, BC, Canada, December 5-8
    • Kilian Q. Weinberger, John Blitzer, Lawrence K. Saul. Distance metric learning for large margin nearest-neighbor classification. in Advances in Neural Information Processing Systems 18, Vancouver, BC, Canada, December 5-8, 2005.
    • (2005) Advances in Neural Information Processing Systems 18
    • Weinberger, K.Q.1    Blitzer, J.2    Saul, L.K.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.