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Volumn 15, Issue , 2014, Pages 1431-1453

Adaptive sampling for large scale boosting

Author keywords

Boosting; Feature selection; Large scale learning

Indexed keywords

ADAPTIVE BOOSTING; ALGORITHMS; FEATURE EXTRACTION; OBJECT RECOGNITION;

EID: 84901634066     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Article
Times cited : (12)

References (20)
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    • Auer, P.1    Cesa-Bianchi, N.2    Fischer, P.3
  • 5
    • 77956554273 scopus 로고    scopus 로고
    • Fast Boosting using adversarial bandits
    • R. Busa-Fekete and B. Kegl. Fast Boosting using adversarial bandits. In ICML, 2010.
    • (2010) ICML
    • Busa-Fekete, R.1    Kegl, B.2
  • 9
    • 37049036831 scopus 로고    scopus 로고
    • Priority sampling for estimation of arbitrary subset sums
    • December
    • N. Dufield, C. Lund, and M. Thorup. Priority sampling for estimation of arbitrary subset sums. J. ACM, 54, December 2007.
    • (2007) J ACM , vol.54
    • Dufield, N.1    Lund, C.2    Thorup, M.3
  • 10
    • 84974712696 scopus 로고    scopus 로고
    • Boosting applied to word sense disambiguation
    • G. Escudero, L. Mfiarquez, and G. Rigau. Boosting applied to word sense disambiguation. Machine Learning: ECML 2000, pages 129-141, 2000.
    • (2000) Machine Learning: ECML , vol.2000 , pp. 129-141
    • Escudero, G.1    Mfiarquez, L.2    Rigau, G.3
  • 11
    • 84932617705 scopus 로고    scopus 로고
    • Learning generative visual models from few training examples: An incremental Bayesian approach tested on 101 object categories
    • L. Fei-Fei, R. Fergus, and P. Perona. Learning generative visual models from few training examples: an incremental Bayesian approach tested on 101 object categories. In CVPR, Workshop on Generative-Model Based Vision, 2004. http://www.vision.caltech.edu/ Image-Datasets/Caltech101/.
    • (2004) CVPR, Workshop on Generative-Model Based Vision
    • Fei-Fei, L.1    Fergus, R.2    Perona, P.3
  • 17
    • 77956002520 scopus 로고    scopus 로고
    • Learning multiple layers of features from tiny images
    • A. Krizhevsky. Learning multiple layers of features from tiny images. Technical report, 2009. http://www.cs.toronto.edu/~kriz/cifar.html.
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    • Krizhevsky, A.1
  • 18
    • 0032203257 scopus 로고    scopus 로고
    • Gradient-based learning applied to doc- ument recognition
    • Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. Gradient-based learning applied to doc- ument recognition. In Proceedings of the IEEE, volume 86(11), pages 2278-2324, 1998. http://yann.lecun.com/exdb/mnist/.
    • (1998) Proceedings of the IEEE , vol.86 , Issue.11 , pp. 2278-2324
    • Lecun, Y.1    Bottou, L.2    Bengio, Y.3    Haffner, P.4
  • 20
    • 0033281701 scopus 로고    scopus 로고
    • Improved boosting algorithms using confidence-rated predictions
    • DOI 10.1023/A:1007614523901
    • R. E. Schapire and Y. Singer. Improved boosting algorithms using condence-rated predic- tions. Machine learning, 37(3):297-336, 1999. (Pubitemid 32210620)
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    • Schapire, R.E.1    Singer, Y.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.