메뉴 건너뛰기




Volumn 18, Issue 5, 2004, Pages 309-323

Support vector machines and gradient boosting for graphical estimation of a slate deposit

Author keywords

Boosting; Kernels; Slate; Spatial statistics; SVM

Indexed keywords

BUILDING STONE; GEOSTATISTICS; MINING; RESOURCE MANAGEMENT; SLATE;

EID: 7544230053     PISSN: 14363240     EISSN: None     Source Type: Journal    
DOI: 10.1007/s00477-004-0185-5     Document Type: Article
Times cited : (33)

References (61)
  • 1
    • 0003501215 scopus 로고    scopus 로고
    • A review of gaussian random fields and correlation functions
    • Technical Report, Norwegian Computing Center, Oslo
    • Abrahamsen P (1997) A review of gaussian random fields and correlation functions. Technical Report, Norwegian Computing Center, Oslo
    • (1997)
    • Abrahamsen, P.1
  • 4
    • 0000275022 scopus 로고    scopus 로고
    • Prediction games and arcing algorithms
    • Breiman L (1999) Prediction games and arcing algorithms. Neural Comput 11:1493-1517
    • (1999) Neural. Comput. , vol.11 , pp. 1493-1517
    • Breiman, L.1
  • 5
    • 0026954346 scopus 로고
    • Forecasting the behavior of multivariate time series using neural networks
    • Chakraborty K, Mehrotra K, Mohan CK, Ranka S (1992) Forecasting the behavior of multivariate time series using neural networks. Neural Netw 5:961-970
    • (1992) Neural Netw. , vol.5 , pp. 961-970
    • Chakraborty, K.1    Mehrotra, K.2    Mohan, C.K.3    Ranka, S.4
  • 6
    • 0026116468 scopus 로고
    • Orthogonal least squares learning algorithm for radial basis function networks
    • Chen S, Cowan CFN, Grant PM (1991) Orthogonal least squares learning algorithm for radial basis function networks. IEEE Transactions on Neural Netw 2:302-309
    • (1991) IEEE Transactions on Neural Netw. , vol.2 , pp. 302-309
    • Chen, S.1    Cowan, C.F.N.2    Grant, P.M.3
  • 7
    • 84949203556 scopus 로고    scopus 로고
    • Locally regularised orthogonal least squares algorithm for the construction of sparse kernel regression models
    • Chen S (2002) Locally regularised orthogonal least squares algorithm for the construction of sparse kernel regression models. In: Proceedings of the 6th international conference on signal processing pp 1229-1232
    • (2002) Proceedings of the 6th International Conference on Signal Processing , pp. 1229-1232
    • Chen, S.1
  • 11
    • 0000913324 scopus 로고    scopus 로고
    • SVMTorch: Support vector machines for large-scale regression problems
    • Collobert R, Bengio S (2001) SVMTorch: Support vector machines for large-scale regression problems. J Mach Learn 1:143-160
    • (2001) J. Mach. Learn , vol.1 , pp. 143-160
    • Collobert, R.1    Bengio, S.2
  • 12
    • 34249753618 scopus 로고
    • Support-vector networks
    • Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20:273-297
    • (1995) Mach. Learn , vol.20 , pp. 273-297
    • Cortes, C.1    Vapnik, V.2
  • 16
    • 0002531537 scopus 로고
    • Projection-based approximation and a duality with kernel methods
    • Donoho DL, Johnstone IM (1989) Projection-based approximation and a duality with kernel methods. Ann Stat 17:58-106
    • (1989) Ann. Stat. , vol.17 , pp. 58-106
    • Donoho, D.L.1    Johnstone, I.M.2
  • 20
    • 0031211090 scopus 로고    scopus 로고
    • A decision-theoretic generalization of on-line learning and an application to boosting
    • Freund Y, Schapire RE (1997) A decision-theoretic generalization of on-line learning and an application to boosting. J Compu Syst Sci 55:119-139
    • (1997) J. Compu. Syst. Sci. , vol.55 , pp. 119-139
    • Freund, Y.1    Schapire, R.E.2
  • 22
    • 0003591748 scopus 로고    scopus 로고
    • Greedy function approximation: A gradient boosting machine
    • Technical report, Stanford University
    • Friedman JH (1999) Greedy function approximation: A gradient boosting machine. Technical report, Stanford University
    • (1999)
    • Friedman, J.H.1
  • 23
    • 0034164230 scopus 로고    scopus 로고
    • Additive Logistic Regression: A statistical view of boosting
    • Friedman JH, Hastie T, Tibshirani R (2000) Additive Logistic Regression: A statistical view of boosting. Ann Stat 28:337-407
    • (2000) Ann. Stat. , vol.28 , pp. 337-407
    • Friedman, J.H.1    Hastie, T.2    Tibshirani, R.3
  • 25
    • 0038259114 scopus 로고    scopus 로고
    • Classes of kernels for machine learning: A statistical perspective
    • Genton MG (2001) Classes of kernels for machine learning: A statistical perspective. J Mach Learn Res 2:299-312
    • (2001) J. Mach. Learn Res. , vol.2 , pp. 299-312
    • Genton, M.G.1
  • 26
    • 7544223962 scopus 로고    scopus 로고
    • Local machine learning models for spatial data analysis
    • Gilardi N, Bengio S (2000) Local machine learning models for spatial data analysis. J Geogra Inform Dec Anal 4:11-28
    • (2000) J. Geogra Inform. Dec. Anal. , vol.4 , pp. 11-28
    • Gilardi, N.1    Bengio, S.2
  • 27
    • 7544236831 scopus 로고    scopus 로고
    • Comparison of four machine learning algorithms for spatial data analysis
    • Dubois G, Malczewski J, De Cort M (eds) european comissioin joint research centre. Office for official publications of the European Communities
    • Gilardi N, Bengio S (2003) Comparison of four machine learning algorithms for spatial data analysis. In: Dubois G, Malczewski J, De Cort M (eds) Mapping radioactivity in the environment. Spatial interpolation comparison 97. european comissioin joint research centre. Office for official publications of the European Communities, pp 222-237
    • (2003) Mapping Radioactivity in the Environment. Spatial Interpolation Comparison 97 , pp. 222-237
    • Gilardi, N.1    Bengio, S.2
  • 28
    • 0001219859 scopus 로고
    • Regularization theory and neural networks architectures
    • Girosi F, Jones M, Poggio T (1995), Regularization theory and neural networks architectures. Neural Comput 7:219-269
    • (1995) Neural. Comput. , vol.7 , pp. 219-269
    • Girosi, F.1    Jones, M.2    Poggio, T.3
  • 31
    • 0002714543 scopus 로고    scopus 로고
    • Making large-scale support vector machine learning practical
    • Schölkopf B, Burges C Smola A (eds) MIT Press
    • Joachims T (1999) Making large-scale support vector machine learning practical. In: Schölkopf B, Burges C Smola A (eds) Advances in Kernel Methods. MIT Press
    • (1999) Advances in Kernel Methods
    • Joachims, T.1
  • 32
    • 0000523636 scopus 로고
    • On a conjecture of Huber concerning the convergence of projection pursuit regression
    • Jones LK (1987) On a conjecture of Huber concerning the convergence of projection pursuit regression. Ann Stat 15:880-882
    • (1987) Ann. Stat. , vol.15 , pp. 880-882
    • Jones, L.K.1
  • 35
    • 7544244828 scopus 로고    scopus 로고
    • Advanced spatial data analysis and modelling with support vector machines
    • Kanevski M, Pozdnukhov A, Canu S, Maignan M (2002) Advanced spatial data analysis and modelling with support vector machines. Int J Fuzzy Sys 4:606-616
    • (2002) Int. J. Fuzzy Sys. , vol.4 , pp. 606-616
    • Kanevski, M.1    Pozdnukhov, A.2    Canu, S.3    Maignan, M.4
  • 36
    • 0000406385 scopus 로고
    • A correspondence between bayesian estimation on stochastic processes and smoothing by splines
    • Kimeldorf G, Wahba G (1970) A correspondence between bayesian estimation on stochastic processes and smoothing by splines. Ann Math Stat 41:495-502
    • (1970) Ann. Math Stat. , vol.41 , pp. 495-502
    • Kimeldorf, G.1    Wahba, G.2
  • 37
    • 2942611495 scopus 로고    scopus 로고
    • Evaluation of interpolation accuracy of neural kriging with application to temperature-distribution analysis
    • Koike K, Matsuda S, Gu B (2001) Evaluation of interpolation accuracy of neural kriging with application to temperature-distribution analysis. Math Geol 33:421-448
    • (2001) Math Geol. , vol.33 , pp. 421-448
    • Koike, K.1    Matsuda, S.2    Gu, B.3
  • 38
    • 0003319647 scopus 로고    scopus 로고
    • Introduction to Gaussian processes
    • Bishop CM (ed) NATO Asi Series F, Computer and Systems Sciences. Morgan Kaufmann
    • MacKay DJC (1998) Introduction to Gaussian processes. In: Bishop CM (ed) Neural networks and machine learning. NATO Asi Series F, Computer and Systems Sciences. Morgan Kaufmann, pp 133-165
    • (1998) Neural Networks and Machine Learning , pp. 133-165
    • MacKay, D.J.C.1
  • 39
    • 0027842081 scopus 로고
    • Matching pursuit in a time-frequency dictionary
    • Mallat S, Zhang Z (1993) Matching pursuit in a time-frequency dictionary. IEEE Trans Signal Process 41:3397-3415
    • (1993) IEEE Trans. Signal Process , vol.41 , pp. 3397-3415
    • Mallat, S.1    Zhang, Z.2
  • 40
    • 7544228822 scopus 로고    scopus 로고
    • Comparison of Kriging and Neural Networks with Application to the Exploitation of a Slate Mine
    • Accepted for publication in Math Geol
    • Matías JM, Vaamonde A, Taboada J, González-Manteiga W (2003) Comparison of Kriging and Neural Networks with Application to the Exploitation of a Slate Mine. Accepted for publication in Math Geol
    • (2003)
    • Matías, J.M.1    Vaamonde, A.2    Taboada, J.3    González-Manteiga, W.4
  • 41
    • 7544233393 scopus 로고    scopus 로고
    • Regularized kriging: The support vectors method applied to kriging
    • ICANN-ICONIP'2003. Lecture notes in computer science Springer
    • Matías JM, González-Manteiga W (2003) Regularized kriging: The support vectors method applied to kriging. In: Artificial neural networks and neural information processing, ICANN-ICONIP'2003. Lecture notes in computer science, vol. 2714, Springer
    • (2003) Artificial Neural Networks and Neural Information Processing , vol.2714
    • Matías, J.M.1    González-Manteiga, W.2
  • 42
    • 0031334889 scopus 로고    scopus 로고
    • An improved training algorithm for support vector machines
    • Principe J, Gile L, Morgan N, Wilson E (eds) Proceedings of the 1997 IEEE workshop
    • Osuna E, Freund R, Girosi F (1997) An improved training algorithm for support vector machines. In: Principe J, Gile L, Morgan N, Wilson E (eds) Neural networks for signal processing VII. Proceedings of the 1997 IEEE workshop, pp 276-285
    • (1997) Neural Networks for Signal Processing VII , pp. 276-285
    • Osuna, E.1    Freund, R.2    Girosi, F.3
  • 44
    • 0003120218 scopus 로고    scopus 로고
    • Fast training of support vector machines using sequential minimal optimization
    • Schölkopf B, Burges C, Smola A (eds) MIT Press
    • Platt J (1999) Fast training of support vector machines using sequential minimal optimization. In: Schölkopf B, Burges C, Smola A (eds) Advances in Kernel Methods. MIT Press
    • (1999) Advances in Kernel Methods
    • Platt, J.1
  • 47
    • 0025448521 scopus 로고
    • The strength of weak learnability
    • Schapire RE (1990) The strength of weak learnability. Mach Learn 5:197-227
    • (1990) Mach. Learn , vol.5 , pp. 197-227
    • Schapire, R.E.1
  • 48
    • 0032280519 scopus 로고    scopus 로고
    • Boosting the margin: A new explanation for the effectiveness of voting methods
    • Schapire RE, Freund Y, Bartlett Y, Lee W (1998) Boosting the margin: A new explanation for the effectiveness of voting methods. Ann Stat 26:1651-1686
    • (1998) Ann. Stat. , vol.26 , pp. 1651-1686
    • Schapire, R.E.1    Freund, Y.2    Bartlett, Y.3    Lee, W.4
  • 51
    • 84898946392 scopus 로고    scopus 로고
    • Semiparametric support vector and linear programming machines
    • Kearns MS, Solla SA, Cohn DA (eds) MIT press
    • Smola A, Frieâ J, Schölkopf B (1999) Semiparametric support vector and linear programming machines. In: Kearns MS, Solla SA, Cohn DA (eds) Advances in neural information processing systems 11. MIT press, pp 585-591
    • (1999) Advances in Neural Information Processing Systems 11 , pp. 585-591
    • Smola, A.1    Frieâ, J.2    Schölkopf, B.3
  • 52
    • 0003401675 scopus 로고    scopus 로고
    • A tutorial on support vector regression
    • Technical report NC2-TR-1998-030, NeuroCOLT2
    • Smola A, Schölkopf B (1998) A tutorial on support vector regression. Technical report NC2-TR-1998-030, NeuroCOLT2, http://www.neurocolt.com
    • (1998)
    • Smola, A.1    Schölkopf, B.2
  • 53
    • 0031554733 scopus 로고    scopus 로고
    • Application of geostatistical techniques to exploitation planning in slate quarries
    • Taboada J, Vaamonde A, Saavedra A, Alejano L (1997) Application of geostatistical techniques to exploitation planning in slate quarries. Eng Geol 47:269-277
    • (1997) Eng. Geol. , vol.47 , pp. 269-277
    • Taboada, J.1    Vaamonde, A.2    Saavedra, A.3    Alejano, L.4
  • 56
    • 0003551703 scopus 로고    scopus 로고
    • LOQO: An interior point code for quadratic programming
    • Technical report, SOR-94-15, Statistics and operations research, princeton university, edu/rvdb /techrepspshtml
    • Vanderbei RJ (1998) LOQO: An interior point code for quadratic programming. Technical report, SOR-94-15, Statistics and operations research, princeton university, http://www.princeton. edu/rvdb /techrepspshtml
    • (1998)
    • Vanderbei, R.J.1
  • 57
    • 0003969585 scopus 로고
    • Estimation of dependences based on empirical data
    • Springer-Verlag
    • Vapnik V (1982) Estimation of dependences based on empirical data. Springer-Verlag
    • (1982)
    • Vapnik, V.1
  • 60
    • 0026439790 scopus 로고
    • Evaluation and comparison of spatial interpolators
    • Weber DD, Englund EJ (1992) Evaluation and comparison of spatial interpolators. Math Geol 24:381-391
    • (1992) Math. Geol. , vol.24 , pp. 381-391
    • Weber, D.D.1    Englund, E.J.2
  • 61
    • 0028602690 scopus 로고
    • Evaluation and comparison of spatial interpolators, II
    • Weber DD, Englund EJ (1994) Evaluation and comparison of spatial interpolators, II. Math Geol 26:589-603
    • (1994) Math. Geol. , vol.26 , pp. 589-603
    • Weber, D.D.1    Englund, E.J.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.