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Volumn , Issue , 2014, Pages 133-148

Machine learning on geospatial big data

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; BIG DATA; LEARNING SYSTEMS; STATISTICS;

EID: 84952640815     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1201/b16524     Document Type: Chapter
Times cited : (8)

References (10)
  • 1
    • 84904136037 scopus 로고    scopus 로고
    • Large-scale machine learning with stochastic gradient descent
    • Princeton, NJ: Physica-Verlag HD
    • Bottou, L. 2010. Large-scale machine learning with stochastic gradient descent. Proceedings of COMPSTAT’2010. Princeton, NJ: Physica-Verlag HD. pp. 177-186.
    • (2010) Proceedings of COMPSTAT’2010 , pp. 177-186
    • Bottou, L.1
  • 2
    • 80053446757 scopus 로고    scopus 로고
    • An analysis of single-layer networks in unsupervised feature learning
    • Coates, A., H. Lee, and A. Y. Ng. 2010. An analysis of single-layer networks in unsupervised feature learning. Ann Arbor, 1001: 48109.
    • (2010) Ann Arbor , vol.1001 , pp. 48109
    • Coates, A.1    Lee, H.2    Ng, A.Y.3
  • 3
    • 7544236831 scopus 로고    scopus 로고
    • Comparison of four machine learning algorithms for spatial data analysis
    • Office for Official Publications of the European Communities, Luxembourg
    • Gilardi, N. et al. 2003. Comparison of four machine learning algorithms for spatial data analysis. Mapping Radioactivity in the Environment-Spatial Interpolation Comparison, Office for Official Publications of the European Communities, Luxembourg, Vol. 97, pp. 222-237.
    • (2003) Mapping Radioactivity in the Environment-Spatial Interpolation Comparison , vol.97 , pp. 222-237
    • Gilardi, N.1
  • 4
    • 84986232384 scopus 로고    scopus 로고
    • Complexity of predictive neural networks
    • Berlin, Germany: Springer Berlin Heidelberg
    • Kon, M. A. et al. 2006. Complexity of predictive neural networks. Unifying Themes in Complex Systems. Berlin, Germany: Springer Berlin Heidelberg. pp. 181-191.
    • (2006) Unifying Themes in Complex Systems , pp. 181-191
    • Kon, M.A.1
  • 6
    • 0034274591 scopus 로고    scopus 로고
    • A comparison of prediction accuracy, complexity, and training time of thirty-three old and new classification algorithms
    • Lim, T., W. Loh, and Y. Shih. 2000. A comparison of prediction accuracy, complexity, and training time of thirty-three old and new classification algorithms. Machine Learning 40(3): 203-228.
    • (2000) Machine Learning , vol.40 , Issue.3 , pp. 203-228
    • Lim, T.1    Loh, W.2    Shih, Y.3
  • 7
    • 0004255908 scopus 로고    scopus 로고
    • 1997, Burr Ridge, IL: McGraw Hill
    • Mitchell, T. M. 1997. Machine Learning. 1997, Burr Ridge, IL: McGraw Hill, p. 45.
    • (1997) Machine Learning , pp. 45
    • Mitchell, T.M.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.