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Volumn 11, Issue , 2010, Pages 2635-2670

Learnability, stability and uniform convergence

Author keywords

Learnability; Stability; Statistical learning theory; Stochastic convex optimization; Uniform convergence

Indexed keywords

EMPIRICAL RISK MINIMIZATION; EMPIRICAL RISKS; GENERAL LEARNING; LEARNABILITY; LEARNING PROBLEM; NON-TRIVIAL; STATISTICAL LEARNING; STATISTICAL LEARNING THEORY; STOCHASTIC CONVEX OPTIMIZATION; SUFFICIENT CONDITIONS; SUPERVISED CLASSIFICATION; UNIFORM CONVERGENCE;

EID: 78649409695     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Article
Times cited : (508)

References (27)
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    • Learning theory: Stability is sufficient for generalization and necessary and sufficient for consistency of empirical risk minimization
    • S. Mukherjee, P. Niyogi, T. Poggio, and R. Rifkin. Learning theory: stability is sufficient for generalization and necessary and sufficient for consistency of empirical risk minimization. Advances in Computational Mathematics, 25 (1-3): 161-193, 2006.
    • (2006) Advances in Computational Mathematics , vol.25 , Issue.1-3 , pp. 161-193
    • Mukherjee, S.1    Niyogi, P.2    Poggio, T.3    Rifkin, R.4
  • 15
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.