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Volumn , Issue , 2008, Pages 33-44

Does unlabeled data provably help? Worst-case analysis of the sample complexity of semi-supervised learning

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION PREDICTION; CLUSTER ASSUMPTIONS; DISTRIBUTION-FREE; POTENTIAL BENEFITS; PREDICTION PERFORMANCE; SAMPLE COMPLEXITY; SEMI-SUPERVISED LEARNING; WORST-CASE ANALYSIS;

EID: 77956501439     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (134)

References (15)
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    • Avrim Blum and Tom M. Mitchell. Combining labeled and unlabeled sata with co-training. In COLT, pages 92-100, 1998.
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    • Blum, A.1    Mitchell, T.M.2
  • 8
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    • Risks of semi-supervised learning: How unlabeled data can degrade performance of generative classifiers
    • Olivier Chapelle, Bernhard Schölkopf, and Alexander Zien, editors chapter 4 MIT Press, September
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    • Cozman, F.1    Cohen, I.2
  • 9
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    • Ran El-Yaniv and Dmitry Pechyony. Stable transductive learning. In COLT, pages 35-49, 2006.
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    • El-Yaniv, R.1    Pechyony, D.2
  • 10
    • 38049049130 scopus 로고    scopus 로고
    • Transductive rademacher complexity and its applications
    • Ran El-Yaniv and Dmitry Pechyony. Transductive rademacher complexity and its applications. In COLT, pages 157-171, 2007.
    • (2007) COLT , pp. 157-171
    • El-Yaniv, R.1    Pechyony, D.2
  • 11
    • 0031620209 scopus 로고    scopus 로고
    • Improved lower bounds for learning from noisy examples: And information-theoretic approach
    • ACM
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  • 12
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.