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Volumn , Issue , 2013, Pages 1-9

Fast exact max-kernel search

Author keywords

[No Author keywords available]

Indexed keywords

ABSTRACT OBJECT; FEATURE REPRESENTATION; METRIC SPACES; ORDERS OF MAGNITUDE; SEARCH PROBLEM; SIMILARITY SEARCH;

EID: 84937874334     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1137/1.9781611972832.1     Document Type: Conference Paper
Times cited : (50)

References (36)
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    • 85008044987 scopus 로고    scopus 로고
    • Matrix factorization techniques for recommender systems
    • Y. Koren, R. M. Bell, and C. Volinsky. Matrix Factorization Techniques for Recommender Systems. In IEEE Computer, 2009.
    • (2009) IEEE Computer
    • Koren, Y.1    Bell, R.M.2    Volinsky, C.3
  • 18
    • 0033906160 scopus 로고    scopus 로고
    • PAC nearest neighbor queries: Approximate and controlled search in highdimensional and metric spaces
    • P. Ciaccia and M. Patella. PAC Nearest Neighbor Queries: Approximate and Controlled Search in Highdimensional and Metric spaces. In Proc. of 16th International Conf. on Data Engineering, 2000.
    • (2000) Proc. of 16th International Conf. on Data Engineering
    • Ciaccia, P.1    Patella, M.2
  • 19
    • 84863385458 scopus 로고    scopus 로고
    • Rank-approximate nearest neighbor search: Retaining meaning and speed in high dimensions
    • P. Ram, D. Lee, H. Ouyang, and A. G. Gray. Rank-Approximate Nearest Neighbor Search: Retaining Meaning and Speed in High Dimensions. In Advances in Neural Info. Processing Systems 22, 2009.
    • (2009) Advances in Neural Info. Processing Systems , vol.22
    • Ram, P.1    Lee, D.2    Ouyang, H.3    Gray, A.G.4
  • 24
    • 1942419246 scopus 로고    scopus 로고
    • The anchors hierarchy: Using the triangle inequality to survive high dimensional data
    • A. W. Moore. The Anchors Hierarchy: Using the triangle inequality to survive high dimensional data. In Proc. of Uncertainty in Artificial Intelligence, 2000.
    • (2000) Proc. of Uncertainty in Artificial Intelligence
    • Moore, A.W.1
  • 35
    • 84880205736 scopus 로고    scopus 로고
    • A distributed kernel summation framework for general-dimension machine learning
    • D. Lee, R. Vuduc, and A. G. Gray. A distributed kernel summation framework for general-dimension machine learning. In SIAM Intl. Conf. on Data Mining, 2012.
    • (2012) SIAM Intl. Conf. on Data Mining
    • Lee, D.1    Vuduc, R.2    Gray, A.G.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.