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Volumn 4539 LNAI, Issue , 2007, Pages 157-171

Transductive rademacher complexity and its applications

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; BRANCH AND BOUND METHOD; DATA REDUCTION; ERROR ANALYSIS; GRAPH THEORY; LEARNING SYSTEMS;

EID: 38049049130     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-72927-3_13     Document Type: Conference Paper
Times cited : (28)

References (23)
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    • Balcan, M.F.1    Blum, A.2
  • 3
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    • Rademacher and Gaussian complexities: Risk bounds and structural results
    • Bartlett, P., Mendelson, S.: Rademacher and Gaussian complexities: risk bounds and structural results. Journal of Machine Learning Research 3, 463-482 (2002)
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    • Bartlett, P.1    Mendelson, S.2
  • 4
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    • Belkin, M., Matveeva, I., Niyogi, P.: Regularization and semi-supervised learning on large graphs. In: Shawe-Taylor, J., Singer, Y. (eds.) COLT 2004. LNCS (LNAI), 3120, pp. 624-638. Springer, Heidelberg (2004)
    • Belkin, M., Matveeva, I., Niyogi, P.: Regularization and semi-supervised learning on large graphs. In: Shawe-Taylor, J., Singer, Y. (eds.) COLT 2004. LNCS (LNAI), vol. 3120, pp. 624-638. Springer, Heidelberg (2004)
  • 5
    • 3142725535 scopus 로고    scopus 로고
    • Semi-supervised learning on Riemannian manifolds
    • Belkin, M., Niyogi, P.: Semi-supervised learning on Riemannian manifolds. Machine Learning 56, 209-239 (2004)
    • (2004) Machine Learning , vol.56 , pp. 209-239
    • Belkin, M.1    Niyogi, P.2
  • 6
    • 9444288055 scopus 로고    scopus 로고
    • Blum, A., Langford, J.: PAC-MDL Bounds. In: COLT, pp. 344-357. Springer, Heidelberg (2003)
    • Blum, A., Langford, J.: PAC-MDL Bounds. In: COLT, pp. 344-357. Springer, Heidelberg (2003)
  • 9
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    • Explicit learning curves for transduction and application to clustering and compression algorithms
    • Derbeko, P., El-Yaniv, R., Meir, R.: Explicit learning curves for transduction and application to clustering and compression algorithms. Journal of Artificial Intelligence Research 22, 117-142 (2004)
    • (2004) Journal of Artificial Intelligence Research , vol.22 , pp. 117-142
    • Derbeko, P.1    El-Yaniv, R.2    Meir, R.3
  • 11
    • 24344472681 scopus 로고    scopus 로고
    • Effective transductive learning via objective model selection
    • El-Yaniv, R., Gerzon, L.: Effective transductive learning via objective model selection. Pattern Recognition Letters 26, 2104-2115 (2005)
    • (2005) Pattern Recognition Letters , vol.26 , pp. 2104-2115
    • El-Yaniv, R.1    Gerzon, L.2
  • 13
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    • An analysis of graph cut size for transductive learning
    • Hanneke, S.: An analysis of graph cut size for transductive learning. In: ICML, pp. 393-399 (2006)
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    • Hanneke, S.1
  • 14
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    • Online learning over graphs
    • Herbster, M., Pontil, M., Wainer, L.: Online learning over graphs. In: ICML, pp. 305-312 (2005)
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  • 17
    • 3142691501 scopus 로고    scopus 로고
    • Generalization error bounds for Bayesian Mixture Algorithms
    • Meir, R., Zhang, T.: Generalization error bounds for Bayesian Mixture Algorithms. Journal of Machine Learning Research 4, 839-860 (2003)
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  • 18
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    • A generalized representer theorem
    • Helmbold, D, Williamson, B, eds, COLT 2001 and EuroCOLT 2001, Springer, Heidelberg
    • Scholkopf, B., Herbrich, R., Smola, A.: A generalized representer theorem. In: Helmbold, D., Williamson, B. (eds.) COLT 2001 and EuroCOLT 2001. LNCS (LNAI), vol. 2111, pp. 416-426. Springer, Heidelberg (2001)
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    • Scholkopf, B.1    Herbrich, R.2    Smola, A.3
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    • Analysis of spectral kernel design based semi-supervised learning
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    • Semi-supervised learning using gaussian fields and harmonic functions
    • Zhu, X., Ghahramani, Z., Lafferty, J.D.: Semi-supervised learning using gaussian fields and harmonic functions. In: ICML, pp. 912-919 (2003)
    • (2003) ICML , pp. 912-919
    • Zhu, X.1    Ghahramani, Z.2    Lafferty, J.D.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.