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Volumn 46, Issue 1, 2011, Pages 31-42

Learning minimal abstractions

Author keywords

Concurrency; Heap abstractions; Machine learning; Randomization; Static analysis

Indexed keywords

ALLOCATION SITES; CONCURRENCY; HEAP ABSTRACTIONS; K-VALUES; MACHINE-LEARNING; POINTS-TO ANALYSIS; RANDOMIZATION; TWO MACHINES;

EID: 79251562562     PISSN: 15232867     EISSN: None     Source Type: Journal    
DOI: 10.1145/1925844.1926391     Document Type: Conference Paper
Times cited : (26)

References (27)
  • 1
    • 0000710299 scopus 로고
    • Queries and concept learning
    • D. Angluin. Queries and concept learning. Machine Learning, 2(4):319-342, 1988.
    • (1988) Machine Learning , vol.2 , Issue.4 , pp. 319-342
    • Angluin, D.1
  • 11
    • 53749104898 scopus 로고    scopus 로고
    • Evaluating the benefits of contextsensitive points-to analysis using a BDD-based implementation
    • O. Lhoták and L. Hendren. Evaluating the benefits of contextsensitive points-to analysis using a BDD-based implementation. ACM Transactions on Software Engineering and Methodology, 18(1):1-53, 2008.
    • (2008) ACM Transactions on Software Engineering and Methodology , vol.18 , Issue.1 , pp. 1-53
    • Lhoták, O.1    Hendren, L.2
  • 16
    • 18844379279 scopus 로고
    • Demand interprocedural program analysis using logic databases
    • T. W. Reps. Demand interprocedural program analysis using logic databases. In Workshop on Programming with Logic Databases, pages 163-196, 1993.
    • (1993) Workshop on Programming with Logic Databases , pp. 163-196
    • Reps, T.W.1
  • 23
    • 0021518106 scopus 로고
    • A theory of the learnable
    • L. Valiant. A theory of the learnable. Communications of the ACM, 27(11):1134-1142, 1984.
    • (1984) Communications of the ACM , vol.27 , Issue.11 , pp. 1134-1142
    • Valiant, L.1
  • 24
    • 65749083666 scopus 로고    scopus 로고
    • Sharp thresholds for noisy and high-dimensional recovery of sparsity using l1-constrained quadratic programming (lasso)
    • M. J. Wainwright. Sharp thresholds for noisy and high-dimensional recovery of sparsity using l1-constrained quadratic programming (lasso). IEEE Transactions on Information Theory, 55:2183-2202, 2009.
    • (2009) IEEE Transactions on Information Theory , vol.55 , pp. 2183-2202
    • Wainwright, M.J.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.